Systems and Methods for Generating and Applying Matrix Images to Monitor Cardiac Disease

ABSTRACT

Systems and methods are provided for monitoring progression of a cardiac disease in a patient by providing cardio-vibrational image matrixes and/or ECG image matrices generated using sensor data supplied by a medical device. In some examples, cardio-vibrational image matrices and/or ECG image matrices are output as image files. In some implementations, systems and methods are provided for using such cardio-vibrational image matrices and/or an ECG image matrices, and/or other clinical information, using machine learning classifiers, to assess cardiac risk in a patient. In some implementations, systems and methods are provided for using cardio-vibrational image matrixes and/or ECG image matrices, and/or other clinical information for real-time analysis of cardiac risk.

RELATED APPLICATIONS

This application is a continuation of and claims priority to U.S. Pat.Application Serial No. 16/938,244, entitled “Systems and Methods forGenerating and Applying Matrix Images to Monitor Cardiac Disease,” filedJul. 24, 2020. All above identified applications are hereby incorporatedby reference in their entireties.

BACKGROUND

Electrical shock therapy to the heart was developed to rapidly andeffectively terminate potentially lethal cardiac arrhythmias. There are2 main types of electrical shock therapy. Cardioversion is an electricshock timed to the underlying heartbeat. Defibrillation is an electricshock not timed to the heart’s electrical signal.

There are many different kinds of heart rhythms, some of which anelectrical shock is appropriate treatment (“shockable rhythm”) and someof them a shock would not be appropriate treatment (“non-shockablerhythm”). For example, normal sinus rhythm is a person’s normal heartrhythm. There are, however, many abnormal rhythms that could not betreated with an electrical shock. There are also some abnormal rhythmsthat do not benefit from shocks even though they are potentially lethalarrhythmias, which means that the patient cannot remain alive with therhythm, but yet applying shocks will not help convert the rhythm.

As an example of a non-shockable rhythm, if a patient experiencesasystole (the absence of a heartbeat), application of shocks will beineffective. Pacing the heart, for example, would be one treatment forasystole. Electrical shocks are also not recommended for the treatmentof bradycardias, during which the heart beats too slowly, even thoughthis could be lethal. Electro-mechanical dissociation (EMD), in whichthere is electrical activity in the heart but it is not making the heartmuscle contract, is non-shockable and non-viable, and would require CPRas a first response. Idio-ventricular rhythms, in which the normalelectrical activity occurs in the ventricles but not the atria, can alsobe non-shockable and non-viable. Idio-ventricular rhythms can result inslow heart rhythms of 30 or 40 beats per minute, often causing thepatient to lose consciousness. The slow heart rhythm occurs because theventricles ordinarily respond to the activity of the atria, but when theatria stop their electrical activity, a slower, backup rhythm occurs inthe ventricles.

The primary examples of shockable rhythms, for which a first responderor automated defibrillation system should perform defibrillation,include ventricular fibrillation, ventricular tachycardia, andventricular flutter.

Some conventional medical devices that monitor the cardiopulmonarysystem obtain a subject’s electrocardiogram (ECG) signal from bodysurface electrodes. Known ambulatory wearable defibrillators, such asthe LifeVest® Wearable Cardioverter Defibrillator available from ZOLLMedical Corporation of Chelmsford, Mass., use four ECG sensingelectrodes in a dual-channel bipolar configuration. This arrangement ofECG sensing electrodes is usually suitable because in most cases it israre that noise or electrode movement affects the entire bodycircumference. The dual-channel bipolar configuration providesredundancy and allows the system to operate on a single channel ifnecessary. Because signal quality also varies from subject to subject,having two channels provides the opportunity to have improved signalpickup, since the ECG sensing electrodes are located in different bodypositions.

Heart rhythms may also be monitored using vibrational sensors (e.g.,including acoustic sensors and/or audio transducers) to detect andrecord cardio-vibrational signals and the timing of the cardiovibrations, including any one or all of S1, S2, S3, and S4 cardiovibrations. Other cardio-vibrational parameters which may be monitoredby recording cardio-vibrational signals include electromechanicalactivation time (EMAT), percentage of EMAT (% EMAT), systolicdysfunction index (SDI), and left ventricular systolic time (LVST). EMATis generally measured from the onset of the Q wave on the ECG to theclosure of the mitral valve within the S1 cardio vibration. ProlongedEMAT has been associated with reduced left ventricular ejection fraction(LVEF, being a measure of how much blood is being pumped out of the leftventricle of the heart with each contraction). % EMAT is EMAT correctedfor heart rate. % EMAT is related to the efficiency of the pump functionof the heart. SDI is a multiplicative combination of ECG and soundparameters (EMAT, S3, QRS duration, and QR interval). SDI predicts leftventricular systolic dysfunction with high specificity. LVST is definedas the time interval between the S1 and the S2 cardio vibrations. It isthe systolic portion of the cardiac cycle. LVST has some heart ratedependence and tends to be approximately 40% (range 30-50%) of thecardiac cycle but is affected by disease that produces poorcontractility and/or a low ejection fraction.

SUMMARY OF ILLUSTRATIVE EMBODIMENTS

In one aspect, the present disclosure relates to a method for monitoringa progression of a cardiac disease in a patient by providingcardio-vibrational image matrixes generated using sensor data suppliedby a medical device. The method may include: accessing a number ofcardio-vibrational signals obtained by at least one vibrational sensormonitoring a heart of the patient; generating, by processing circuitryfrom the number of cardio-vibrational signals, cardio-vibrationalmeasurements of a predetermined duration, the cardio-vibrationalmeasurements including at least a number of S1 peaks and a number of S2peaks; and transforming, by the processing circuitry, thecardio-vibrational measurements of the predetermined duration into acardio-vibrational image matrix. Transforming may include segmenting thecardio-vibrational measurements of the predetermined duration into anumber of adjacent cardiac portions each having a duration smaller thanthe predetermined duration, and plotting the number of adjacent cardiacportions using a number of pixel characteristic values mapped toparameter values of corresponding cardio-vibrational measurements toproduce the cardio-vibrational image matrix. Plotting may include, onone axis, a time progression of the number of adjacent cardiac portions,and on another axis, the pixel characteristic values of each portion ofthe number of adjacent cardiac portions, where the pixel characteristicvalues of each respective portion include values representing at leastan S1 parameter value corresponding to a respective S1 peak of thenumber of S1 peaks, and an S2 parameter value corresponding to arespective S2 peak of the number of S2 peaks, such that thecardio-vibrational image matrix represents, along the time progressionof the number of adjacent cardiac portions, visible differences intiming and/or and intensity of at least the S1 parameter values of thecardiac portions and the S2 parameter values of the cardiac portions.The method may include outputting the cardio-vibrational image matrix asan image file for use in monitoring the progression of the cardiacdisease in the patient.

The characteristic values may include at least one of a pixel intensityor a pixel hue. The parameter values may include at least one of anamplitude, a phase, or a magnitude. The predetermined duration mayinclude at least one of around 15 seconds, around 30 seconds, around 45seconds, around 90 seconds, around 120 seconds, around 2 minutes, around3 minutes, or around 10 minutes. The predetermined duration may includeat least one of between around 15 seconds and around 30 seconds, betweenaround 30 seconds and around 45 seconds, between around 45 seconds andaround 60 seconds, between around 60 seconds and around 90 seconds,between around 90 seconds and 120 seconds, between around 2 minutes oraround 3 minutes, or between around 3 minutes and around 10 minutes. Theduration smaller than the predetermined duration may include at leastone of around 100 milliseconds, around 1000 milliseconds, around 1second, around 2 seconds, around 5 seconds, or around 10 seconds. Theduration smaller than the predetermined duration may include at leastone of between around 1 millisecond and around 100 milliseconds, between100 milliseconds and around 1000 milliseconds, between around 1 secondand around 2 seconds, between around 2 seconds and around 5 seconds, orbetween around 5 seconds and around 10 seconds.

In some embodiments, each portion of the number of adjacent cardiacportions includes at least two S1 peaks of the number of S1 peaks and atleast two S2 peaks of the number of S2 peaks. Plotting the number ofadjacent cardiac portions may include plotting the pixel characteristicvalues of each portion vertically along a y-axis and plotting the timeprogression of the number of adjacent cardiac portions horizontallyalong an x-axis. The cardio-vibrational image matrix may represent,along the x-axis, visible differences in timing and/or pixelcharacteristic values of a portion of the number of S1 peaks and aportion of the number of S2 peaks.

In some embodiments, the method includes, prior to transforming,registering, by the processing circuitry, a time scale of thecardio-vibrational measurements of the predetermined duration with anumber of R-peaks of an ECG reading of the patient obtained during thepredetermined duration, where segmenting includes segmenting at least inpart according to the registering. The method may include applying, bythe processing circuitry to the cardio-vibrational image matrix, asmoothing algorithm to proximately located pixel characteristic valuesof the number of pixel characteristic values in the cardio-vibrationalimage matrix. The number of pixel characteristic values may includebetween around at least 3 and 16 different colors. Thecardio-vibrational measurements may include a number of S3 peaks.

In some embodiments, the medical device includes a wearable cardiacmonitoring device. The wearable cardiac medical device may include acardiac holter monitor and associated number of ECG electrodes. Themedical device may include a cardiac monitoring and treatment device.The cardiac monitoring and treatment device may include an automatedexternal defibrillator. The cardiac monitoring and treatment device mayinclude a wearable cardioverter defibrillator.

In some embodiments, the method includes transmitting, by the processingcircuitry, the cardio-vibrational image matrix to a remote server. Themethod may include applying, by the processing circuitry, thecardio-vibrational image matrix to a machine learning classifier todetermine an arrhythmia condition in the patient, where the machinelearning classifier is trained to identify at least an existence and anonexistence of an arrhythmia condition in ECG image matrices. Themethod may include applying, by the processing circuitry, thecardio-vibrational image matrix to at least one machine learningclassifier to determine a present classification of a number ofclassifications of at least one cardiac risk biomarker, and generating,by the processing circuitry using the present classification, aprediction of future potential outcome related to the at least onecardiac risk biomarker.

In one aspect, the present disclosure relates to a system for monitoringa progression of a cardiac disease in a patient by providingcardio-vibrational image matrixes generated using sensor data suppliedby a medical device. The system may include a non-volatile computerreadable storage medium configured to store a number ofcardio-vibrational measurements, and operations stored as computerexecutable instructions to a non-transitory computer readable mediaand/or encoded in hardware logic. The operations may be configured toaccess a number of cardio-vibrational signals obtained by at least onevibrational sensor monitoring a heart of the patient, generate, from thenumber of cardio-vibrational signals, cardio-vibrational measurements ofa predetermined duration, the cardio-vibrational measurements includingat least a number of S1 peaks and a number of S2 peaks, where thecardio-vibrational measurements are stored to the non-volatile computerreadable storage medium, and transform the cardio-vibrationalmeasurements of the predetermined duration into a cardio-vibrationalimage matrix. Transforming may include segmenting the cardio-vibrationalmeasurements of the predetermined duration into a number of adjacentcardiac portions each having a duration smaller than the predeterminedduration, and plotting the number of adj acent cardiac portions using anumber of pixel characteristic values mapped to parameter values ofcorresponding cardio-vibrational measurements to produce thecardio-vibrational image matrix. Plotting may include, on one axis, atime progression of the number of adjacent cardiac portions, and onanother axis, the pixel characteristic values of each portion of thenumber of adjacent cardiac portions, where the pixel characteristicvalues of each respective portion include values representing at leastan S1 parameter value corresponding to a respective S1 peak of thenumber of S1 peaks, and an S2 parameter value corresponding to arespective S2 peak of the number of S2 peaks, such that thecardio-vibrational image matrix represents, along the time progressionof the number of adjacent cardiac portions, visible differences intiming and/or and intensity of at least the S1 parameter values of thecardiac portions and the S2 parameter values of the cardiac portions.The operations may be configured to output the cardio-vibrational imagematrix as an image file for use in monitoring the progression of thecardiac disease in the patient.

In some embodiments, the characteristic values include at least one of apixel intensity or a pixel hue. The parameter values may include atleast one of an amplitude, a phase, or a magnitude. The predeterminedduration may include at least one of around 15 seconds, around 30seconds, around 45 seconds, around 90 seconds, around 120 seconds,around 2 minutes, around 3 minutes, or around 10 minutes. Thepredetermined duration may include at least one of between around 15seconds and around 30 seconds, between around 30 seconds and around 45seconds, between around 45 seconds and around 60 seconds, between around60 seconds and around 90 seconds, between around 90 seconds and 120seconds, between around 2 minutes or around 3 minutes, or between around3 minutes and around 10 minutes. The duration smaller than thepredetermined duration may include at least one of around 100milliseconds, around 1000 milliseconds, around 1 second, around 2seconds, around 5 seconds, or around 10 seconds. The duration smallerthan the predetermined duration may include at least one of betweenaround 1 millisecond and around 100 milliseconds, between 100milliseconds and around 1000 milliseconds, between around 1 second andaround 2 seconds, between around 2 seconds and around 5 seconds, orbetween around 5 seconds and around 10 seconds.

In some embodiments, each portion of the number of adjacent cardiacportions includes at least two S1 peaks of the number of S1 peaks and atleast two S2 peaks of the number of S2 peaks. Plotting the number ofadjacent cardiac portions may include plotting the pixel characteristicvalues of each portion vertically along a y-axis and plotting the timeprogression of the number of adjacent cardiac portions horizontallyalong an x-axis. The cardio-vibrational image matrix may represent,along the x-axis, visible differences in timing and/or pixelcharacteristic values of a portion of the number of S1 peaks and aportion of the number of S2 peaks. The operations may be configured to,prior to transforming, register a time scale of the cardio-vibrationalmeasurements of the predetermined duration with a number of R-peaks ofan ECG reading of the patient obtained during the predeterminedduration, where segmenting includes segmenting at least in partaccording to the registering.

In some embodiments, the operations are configured to apply, to thecardio-vibrational image matrix, a smoothing algorithm to proximatelylocated pixel characteristic values of the number of pixelcharacteristic values in the cardio-vibrational image matrix. The numberof pixel characteristic values may include between around at least 3 and16 different colors. The cardio-vibrational measurements may include anumber of S3 peaks.

In some embodiments, the medical device includes a wearable cardiacmonitoring device. The wearable cardiac medical device may include acardiac holter monitor and associated number of ECG electrodes. Themedical device may include a cardiac monitoring and treatment device.The cardiac monitoring and treatment device may include an automatedexternal defibrillator. The cardiac monitoring and treatment device mayinclude a wearable cardioverter defibrillator.

In some embodiments, the operations are configured to transmit thecardio-vibrational image matrix to a remote server. The operations maybe configured to apply the cardio-vibrational image matrix to a machinelearning classifier to determine an arrhythmia condition in the patient,where the machine learning classifier is trained to identify at least anexistence and a nonexistence of an arrhythmia condition in ECG imagematrices. The operations may be configured to apply thecardio-vibrational image matrix to at least one machine learningclassifier to determine a present classification of a number ofclassifications of at least one cardiac risk biomarker, and generate,using the present classification, a prediction of future potentialoutcome related to the at least one cardiac risk biomarker.

In one aspect, the present disclosure relates to a method for monitoringa cardiac condition of a patient using ECG image matrix representationsof ECG signals. The method may include obtaining, in real-time byprocessing circuitry, a number of ECG signals of a patient from at leasttwo ECG electrodes of a wearable medical device, and monitoring, by theprocessing circuitry, for an abnormal rhythm in the patient bygenerating, from the number of ECG signals, ECG measurements of apredetermined duration, transforming the ECG measurements of thepredetermined duration into an ECG image matrix, where transformingincludes segmenting the ECG measurements of the predetermined durationinto a number of adjacent ECG portions each having a duration smallerthan the predetermined duration, and plotting the number of ECG portionsusing a number of pixel characteristic values mapped to parameter valuesof corresponding ECG measurements to produce the ECG image matrix.Monitoring for an abnormal rhythm may include applying the ECG imagematrix to a machine learning classifier to determine an arrhythmiacondition in the patient, where the machine learning classifier istrained to identify at least an existence and a nonexistence of anarrhythmia condition in ECG image matrices.

In some embodiments, the machine learning classifier is trained toidentify at least the existence and the nonexistence of the arrhythmiacondition by identifying noise conditions in the ECG image matrices.Identifying the arrhythmia condition may include classifying thearrhythmia condition based on applying the ECG image matrix to themachine learning classifier, where the machine learning classifier istrained to identify the type of arrhythmia condition using a number ofarrhythmia classifications. The number of arrhythmia classifications mayinclude at least one of a duration, a rate, or a mechanism ofarrhythmia.

In some embodiments, generating the ECG measurements includes detecting,from the number of ECG signals, a number of ECG features including oneor more of a set of R peaks, a set of P peaks, a set of T peaks, or aset of QRS complexes. A beginning of a time scale of the ECG imagematrix may be registered to a deflection feature selected from the setof R peaks, the set of P peaks, the set of T peaks, and the set of QRScomplexes, and the machine learning classifier may have been trainedusing a number of ECG image matrices registered to the deflectionfeature. Segmenting the ECG measurements may involve including at leastone deflection feature in each portion of the number of adjacent ECGportions.

In some embodiments, the type of arrhythmia condition includes at leastone of a supraventricular tachycardia (SVT), a ventricular tachycardia,ventricular fibrillation, tachycardia, bradycardia, asystole, a heartpause condition, pulseless electrical activity, or atrial fibrillation.

In some embodiments, a controller of the wearable medical deviceincludes the processing circuitry. The processing circuitry may bedisposed in a remote computer system. The period of time may be up to 30seconds. The machine learning classifier may include a deep neuralnetwork (DNN) model.

In some embodiments, the method includes determining, by the processingcircuitry from the number of ECG signals, a number of ECG metricsrelating to one or more of heart rate, heart rate variability, PVCburden or counts, activity, noise quantifications, atrial fibrillation,momentary pauses, heart rate turbulence, QRS height, QRS width, changesin a size or a shape of the ECG morphology, cosine R-T, artificialpacing, corrected QT interval, QT variability, T wave width, T wavealternans, T-wave variability, ST segment changes, early repolarization,late potentials, fractionated QRS/HF content, or fractionated T wave/HFcontent. The method may include accessing, by the processing circuitry,one or more non-ECG physiological signals, and generating, by theprocessing circuitry, one or more patient metrics based on the one ormore non-ECG physiological signals. The one or more non-ECGphysiological signals may include cardio-vibrational signals, and theone or more patient metrics may relate to at least one ofelectromechanical activation time (EMAT), left ventricular systolic time(LVST), S1 intensity, S2 intensity, S3 intensity, S4 intensity, S1duration, S2 duration, S3 duration, S4 duration, or heart murmurs. Theone or more non-ECG physiological signals may includeradiofrequency-based physiological signals, and the one or more patientmetrics may relate to at least one of thoracic fluid content, arterialpulse measurements, blood pressure measurements, or heart wall movement.The machine learning classifier may be further trained to identify atleast the existence and nonexistence of the arrhythmia condition basedon the one or more patient metrics.

In some embodiments, the method includes causing, by the processingcircuitry, the wearable medical device to provide an electricaltherapeutic shock to the patient based on determining the arrhythmiacondition in the patient. Causing the wearable medical device to providethe electrical therapeutic shock may include causing the wearablemedical device to provide the electrical therapeutic shock withinbetween at least around 5 seconds and around 2 minutes of an onset ofthe arrhythmia condition in the patient. The electrical therapeuticshock may include at least one of a defibrillating shock or a pacingpulse.

In one aspect, the present disclosure relates to a system for monitoringa cardiac condition of a patient using ECG image matrix representationsof ECG signals. The system may include a non-volatile computer readablestorage medium configured to store a number of ECG measurements, atleast one machine learning classifier trained to identify at least anexistence and a nonexistence of an arrhythmia condition in ECG imagematrices, and operations stored as computer executable instructions to anon-transitory computer readable media and/or encoded in hardware logic.The operations may be configured to obtain, in real-time, a number ofECG signals of the patient from at least two ECG electrodes of awearable medical device, and monitor for an abnormal rhythm in thepatient by generating, from the number of ECG signals, ECG measurementsof a predetermined duration, storing, to the non-volatile computerreadable storage medium, the ECG measurements, and transforming the ECGmeasurements of the predetermined duration into an ECG image matrix.Transforming may include segmenting the ECG measurements of thepredetermined duration into a number of adjacent ECG portions eachhaving a duration smaller than the predetermined duration, and plottingthe number of ECG portions using a number of pixel characteristic valuesmapped to parameter values of corresponding ECG measurements to producethe ECG image matrix. Monitoring for the abnormal rhythm may includeapplying the ECG image matrix to the machine learning classifier todetermine an arrhythmia condition in the patient.

In some embodiments, the machine learning classifier is trained toidentify at least the existence and the nonexistence of the arrhythmiacondition by identifying noise conditions in the ECG image matrices.Identifying the arrhythmia condition may include classifying thearrhythmia condition based on applying the ECG image matrix to themachine learning classifier, where the machine learning classifier istrained to identify the type of arrhythmia condition using a number ofarrhythmia classifications. The number of arrhythmia classifications mayinclude at least one of a duration, a rate, or a mechanism ofarrhythmia.

In some embodiments, generating the ECG measurements includes detecting,from the number of ECG signals, a number of ECG features including oneor more of a set of R peaks, a set of P peaks, a set of T peaks, or aset of QRS complexes. A beginning of a time scale of the ECG imagematrix may be registered to a deflection feature selected from the setof R peaks, the set of P peaks, the set of T peaks, and the set of QRScomplexes, and the machine learning classifier may have been trainedusing a number of ECG image matrices registered to the deflectionfeature. Segmenting the ECG measurements may involve including at leastone deflection feature in each portion of the number of adjacent ECGportions.

In some embodiments, the type of arrhythmia condition includes at leastone of a supraventricular tachycardia (SVT), a ventricular tachycardia,ventricular fibrillation, tachycardia, bradycardia, asystole, a heartpause condition, pulseless electrical activity, or atrial fibrillation.

In some embodiments, the system includes the wearable medical device,where the wearable medical device includes the non-volatile computerreadable storage medium. At least a portion of the non-transitorycomputer readable media and/or hardware logic may be disposed in aremote computer system. The period of time may be up to 30 seconds. Themachine learning classifier may include a deep neural network (DNN)model.

In some embodiments, the operations are configured to determine, by fromthe number of ECG signals, a number of ECG metrics relating to one ormore of heart rate, heart rate variability, PVC burden or counts,activity, noise quantifications, atrial fibrillation, momentary pauses,heart rate turbulence, QRS height, QRS width, changes in a size or ashape of the ECG morphology, cosine R-T, artificial pacing, corrected QTinterval, QT variability, T wave width, T wave alternans, T-wavevariability, ST segment changes, early repolarization, late potentials,fractionated QRS/HF content, or fractionated T wave/HF content. Theoperations may be configured to access one or more non-ECG physiologicalsignals, and generate one or more patient metrics based on the one ormore non-ECG physiological signals. The one or more non-ECGphysiological signals may include cardio-vibrational signals, and theone or more patient metrics may relate to at least one ofelectromechanical activation time (EMAT), left ventricular systolic time(LVST), S1 intensity, S2 intensity, S3 intensity, S4 intensity, S1duration, S2 duration, S3 duration, S4 duration, or heart murmurs. Theone or more non-ECG physiological signals may includeradiofrequency-based physiological signals, and the one or more patientmetrics may relate to at least one of thoracic fluid content, arterialpulse measurements, blood pressure measurements, or heart wall movement.The machine learning classifier may be further trained to identify atleast the existence and nonexistence of the arrhythmia condition basedon the one or more patient metrics.

In some embodiments, the operations are configured to cause the wearablemedical device to provide an electrical therapeutic shock to the patientbased on determining the arrhythmia condition in the patient. Causingthe wearable medical device to provide the electrical therapeutic shockmay include causing the wearable medical device to provide theelectrical therapeutic shock within between at least around 5 secondsand around 2 minutes of an onset of the arrhythmia condition in thepatient. The electrical therapeutic shock may include at least one of adefibrillating shock or a pacing pulse.

In one aspect, the present disclosure relates to a method for estimatingrisk of an adverse cardiac outcome in a patient. The method may includeaccessing, from a non-volatile computer readable storage medium, anumber of ECG measurements obtained from ECG signals of a patient, andtransforming, by the processing circuitry, a time series of the numberof ECG measurements into at least one ECG image matrix. Transforming mayinclude, for each set of one or more sets of ECG measurements,segmenting the ECG measurements into a number of adjacent ECG portionseach having a predetermined duration, where segmenting results in anumber of ECG portions, and plotting the number of ECG portions using anumber of pixel characteristic values mapped to parameter values ofcorresponding ECG measurements to produce a respective matrix of the atleast one ECG image matrix. The method may include applying, by theprocessing circuitry, the at least one ECG image matrix to at least onemachine learning classifier to determine a present classification of thenumber of classifications of the at least one cardiac risk biomarker,where the at least one machine learning classifier was trained toidentify a number of classifications of at least one cardiac riskbiomarker, and generating, by the processing circuitry using the presentclassification, a prediction of future potential outcome in the patientrelated to the at least one cardiac risk biomarker.

In some embodiments, the at least one machine learning classifier wastrained at least in part using a number of ECG image matrices generatedfrom a number of historic ECG readings obtained from the patient.Generating the prediction may include comparing the presentclassification to at least one historic classification of the patient todetermine patient progression related to the at least one cardiac riskbiomarker.

In some embodiments, the at least one cardiac risk biomarker relates toone of sudden cardiac arrest (SCA), low ejection fraction (EF), or heartfailure classification. The at least one cardiac risk biomarker mayinclude low EF, and the number of classifications may include a lessthan 35% classification, a 35 to 39% classification, and a 40% to 54%classification. The at least one cardiac risk biomarker may relate toSCA, and the number of classifications includes one or more ofventricular fibrillation (VF), ventricular tachycardia (VT), pulselesselectrical activity (PEA), or asystole.

In some embodiments, the method includes accessing, from thenon-volatile computer readable storage medium, a number ofcardio-vibrational measurements, the cardio-vibrational measurementsincluding at least a number of S1 peaks and a number of S2 peaks, wherethe number of cardio-vibrational measurements were generated from anumber of cardio-vibrational signals obtained by at least onevibrational sensor monitoring a heart of the patient. The method mayinclude transforming, by the processing circuitry, a time series of thenumber of cardio-vibrational measurements into at least onecardio-vibrational image matrix, where transforming includes, for eachset of set of one or more sets of cardio-vibrational measurements,segmenting the respective set of cardio-vibrational measurements into anumber of adjacent cardiac portions each having a predeterminedduration, where segmenting results in a number of cardiac portions, andplotting the number of cardiac portions using a number of pixelcharacteristic values mapped to parameter values of correspondingcardio-vibrational measurements to produce a respective matrix of the atleast one cardio-vibrational image matrix. Determining the presentclassification may include applying a first cardio-vibrational imagematrix of the at least one cardio-vibrational image matrix to the atleast one machine learning classifier.

In some embodiments, the at least one cardiac risk biomarker is one ofelectromechanical activation over time (EMAT), left ventricular systolictime (LVST), S3 intensity, or S3 width.

In some embodiments, the method includes registering, by the processingcircuitry, a time scale of the segmenting of the one or more sets ofcardio-vibrational measurements to a time scale of the segmenting of theone or more sets of ECG measurements, where each set of the one or moresets of cardio-vibrational measurements corresponds to a respective setof the one or more sets of ECG measurements. The at least one machinelearning classifier may have been trained at least in part using anumber of cardio-vibrational image matrices generated based on a numberof historic cardio-vibrational signals obtained from monitoring theheart of the patient.

In one aspect, the present disclosure relates to a system for estimatingrisk of an adverse cardiac outcome in a patient. The system may includea non-volatile computer readable storage medium including a number ofECG measurements obtained from ECG signals of a patient, at least onemachine learning classifier trained to identify a number ofclassifications of at least one cardiac risk biomarker, and operationsstored as computer executable instructions to a non-transitory computerreadable media and/or encoded in hardware logic. The operations may beconfigured to transform a time series of the number of ECG measurementsinto at least one ECG image matrix, where transforming includes, foreach set of one or more sets of ECG measurements, segmenting the ECGmeasurements into a number of adjacent ECG portions each having apredetermined duration, where segmenting results in a number of ECGportions, and plotting the number of ECG portions using a number ofpixel characteristic values mapped to parameter values of correspondingECG measurements to produce a respective matrix of the at least one ECGimage matrix. The operations may be configured to apply the at least oneECG image matrix to the at least one machine learning classifier todetermine a present classification of the number of classifications ofthe at least one cardiac risk biomarker, and generate, using the presentclassification, a prediction of future potential outcome in the patientrelated to the at least one cardiac risk biomarker.

In some embodiments, the at least one machine learning classifier wastrained at least in part using a number of ECG image matrices generatedfrom a number of historic ECG readings obtained from the patient.Generating the prediction may include comparing the presentclassification to at least one historic classification of the patient todetermine patient progression related to the at least one cardiac riskbiomarker.

In some embodiments, the at least one cardiac risk biomarker relates toone of sudden cardiac arrest (SCA), low ejection fraction (EF), or heartfailure classification. The at least one cardiac risk biomarker mayinclude low EF, and the number of classifications may include a lessthan 35% classification, a 35 to 39% classification, and a 40% to 54%classification. The at least one cardiac risk biomarker may relate toSCA, and the number of classifications may include one or more ofventricular fibrillation (VF), ventricular tachycardia (VT), pulselesselectrical activity (PEA), or asystole.

In some embodiments, the non-volatile computer readable storage mediumincludes a number of cardio-vibrational measurements, thecardio-vibrational measurements including at least a number of S1 peaksand a number of S2 peaks, where the number of cardio-vibrationalmeasurements were generated from a number of cardio-vibrational signalsobtained by at least one vibrational sensor monitoring a heart of thepatient. The operations may be configured to transform a time series ofthe number of cardio-vibrational measurements into at least onecardio-vibrational image matrix, where transforming includes, for eachset of set of one or more sets of cardio-vibrational measurements,segmenting the respective set of cardio-vibrational measurements into anumber of adjacent cardiac portions each having a predeterminedduration, where segmenting results in a number of cardiac portions, andplotting the number of cardiac portions using a number of pixelcharacteristic values mapped to parameter values of correspondingcardio-vibrational measurements to produce a respective matrix of the atleast one cardio-vibrational image matrix. Determining the presentclassification may include applying a first cardio-vibrational imagematrix of the at least one cardio-vibrational image matrix to the atleast one machine learning classifier. The at least one cardiac riskbiomarker may be one of electromechanical activation over time (EMAT),left ventricular systolic time (LVST), S3 intensity, or S3 width. Eachset of the one or more sets of cardio-vibrational measurements maycorrespond to a respective set of the one or more sets of ECGmeasurements. The operations may be configured to register a time scaleof the segmenting of the one or more sets of cardio-vibrationalmeasurements to a time scale of the segmenting of the one or more setsof ECG measurements. The at least one machine learning classifier mayhave been trained at least in part using a number of cardio-vibrationalimage matrices generated based on a number of historiccardio-vibrational signals obtained from monitoring the heart of thepatient.

The foregoing general description of the illustrative implementationsand the following detailed description thereof are merely exemplaryaspects of the teachings of this disclosure and are not restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee. The accompanying drawings, which are incorporatedin and constitute a part of the specification, illustrate one or moreembodiments and, together with the description, explain theseembodiments. The accompanying drawings have not necessarily been drawnto scale. Any values dimensions illustrated in the accompanying graphsand figures are for illustration purposes only and may or may notrepresent actual or preferred values or dimensions. Where applicable,some or all features may not be illustrated to assist in the descriptionof underlying features. In the drawings:

FIG. 1 is a flow chart of an example method for generating acardio-vibrational image matrix from cardio-vibrational signals obtainedthrough monitoring a patient;

FIGS. 2A and 2B illustrate example cardio-vibrational image matrices;

FIGS. 3A and 3B illustrate a flow chart of an example method forgenerating and applying an ECG image matrix for monitoring a cardiaccondition of a patient;

FIG. 4 illustrates an example ECG image matrix;

FIG. 5 is a flow diagram of an example process for collecting signalsfrom a wearable medical device and forming image matrices and supportingmetrics for monitoring a cardiac condition in a patient;

FIG. 6 is a flow diagram of an example process for presenting imagematrix data for review by a clinician;

FIGS. 7A and 7B illustrate example graphic output representing an ECGimage matrix and a cardio-vibrational image matrix for clinician review;

FIG. 8 is a flow diagram of an example process for applying machinelearning classifiers to ECG image matrices and to cardio-vibrationalimage matrices to automatically determine whether to apply an electricaltherapeutic shock to a patient;

FIGS. 9A and 9B are flow diagrams of example processes for applyingmachine learning classifiers to ECG image matrices and tocardio-vibrational image matrices to analyze heart risk in a patient;

FIG. 10 is a block diagram of an example medical device for monitoring acardiac condition of a patient; and

FIGS. 11A-11D illustrate example wearable medical devices for monitoringa cardiac condition of a patient.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

The description set forth below in connection with the appended drawingsis intended to be a description of various, illustrative embodiments ofthe disclosed subject matter. Specific features and functionalities aredescribed in connection with each illustrative embodiment; however, itwill be apparent to those skilled in the art that the disclosedembodiments may be practiced without each of those specific features andfunctionalities.

Reference throughout the specification to “one embodiment” or “anembodiment” means that a particular feature, structure, orcharacteristic described in connection with an embodiment is included inat least one embodiment of the subject matter disclosed. Thus, theappearance of the phrases “in one embodiment” or “in an embodiment” invarious places throughout the specification is not necessarily referringto the same embodiment. Further, the particular features, structures orcharacteristics may be combined in any suitable manner in one or moreembodiments. Further, it is intended that embodiments of the disclosedsubject matter cover modifications and variations thereof.

It must be noted that, as used in the specification and the appendedclaims, the singular forms “a,” “an,” and “the” include plural referentsunless the context expressly dictates otherwise. That is, unlessexpressly specified otherwise, as used herein the words “a,” “an,”“the,” and the like carry the meaning of “one or more.” Additionally, itis to be understood that terms such as “left,” “right,” “top,” “bottom,”“front,” “rear,” “side,” “height,” “length,” “width,” “upper,” “lower,”“interior,” “exterior,” “inner,” “outer,” and the like that may be usedherein merely describe points of reference and do not necessarily limitembodiments of the present disclosure to any particular orientation orconfiguration. Furthermore, terms such as “first,” “second,” “third,”etc., merely identify one of a number of portions, components, steps,operations, functions, and/or points of reference as disclosed herein,and likewise do not necessarily limit embodiments of the presentdisclosure to any particular configuration or orientation.

Furthermore, the terms “approximately,” “about,” “proximate,” “minorvariation,” and similar terms generally refer to ranges that include theidentified value within a margin of 20%, 10% or preferably 5% in certainembodiments, and any values therebetween.

All of the functionalities described in connection with one embodimentare intended to be applicable to the additional embodiments describedbelow except where expressly stated or where the feature or function isincompatible with the additional embodiments. For example, where a givenfeature or function is expressly described in connection with oneembodiment but not expressly mentioned in connection with an alternativeembodiment, it should be understood that the inventors intend that thatfeature or function may be deployed, utilized or implemented inconnection with the alternative embodiment unless the feature orfunction is incompatible with the alternative embodiment.

In one aspect, the present disclosure relates to methods and systems formonitoring progression of cardiac disease in a patient by transformingcardio-vibrational signals (e.g., signals corresponding to heartvibrations detected in a patient) obtained through monitoring a patientinto a cardio-vibrational image matrix. The cardio-vibrational signalsmay be obtained from a sensor of a medical device, such as a wearableheart monitoring device. The cardio-vibrational image matrix visuallyrepresents differences in timing and/or occurrences of S1 peaks, S2peaks, and/or other cardio-vibrational parameters and characteristics,such as S3 peaks, S4 peaks, heart murmurs, among others. Thecardio-vibrational image matrix format is advantageous in providing acomparison mechanism that easily highlights to an end user substantialchanges in timing and/or intensity of S1 peaks, S2 peaks, and/or othercardio-vibrational parameters and characteristics, such as S3 peaks, S4peaks, heart murmurs, among others. For example, such informationcontained in cardio-vibrational signals of a predetermined duration canbe depicted in an image. For instance, such predetermined duration canbe of 30 seconds, 60 seconds, 90 seconds, 180 seconds, 300 seconds, orvalues therebetween. Viewing cardio-vibrational signals of suchdurations can result in a relatively lengthy signal strip of data forviewing compared to a visible size of a cardio-vibrational image matrix.Further, the cardio-vibrational image matrix format is advantageous inproviding a pixel mapping that visibly contrasts S1, S2, S3 and S4parameter values from other parameter values within thecardio-vibrational signal (e.g., via pixel intensity and/or hue asdescribed further below). In this manner, harder to detect or viewparameter values such as S3 and S4 be visibly contrasted from S1 and S2parameter values. In comparison to a clinician reviewing the originalmeasurements obtained from the signal in a cardio-vibrational signalgraph, a clinician reviewing the cardio-vibrational image matrix has theopportunity to key in on significant events captured in thecardio-vibrational signals without detailed and lengthy scrutiny.Further, some significant events which may have been lost ormisinterpreted in the cardio-vibrational graph are rendered readilyapparent in the transformation into the cardio-vibrational image matrixformat. Thus, the cardio-vibrational image matrix advantageouslyprovides clinicians with the ability to carry out detailed analysis anddraw more insights of the contents of the cardio-vibrational signalsthan from reviewing cardio-vibrational signal strips of data.

In some embodiments, to transform the cardio-vibrational measurementsinto a cardio-vibrational image matrix, cardio-vibrational measurementsof a predetermined duration are generated from the cardio-vibrationalsignals. The cardio-vibrational measurements may be segmented intoadjacent cardiac portions. The parameter values of thecardio-vibrational measurements of each cardiac portion may be mapped topixel characteristic values (e.g., hue, intensity) and plotted as acardio-vibrational image matrix having, on one axis, a time progressionof the adjacent cardiac portions and, on another axis, the pixelcharacteristic values of each portion. While references herein to pixelcharacteristics values pertain to hue and/or intensity values, it isappreciated that other pixel characteristics may be substituted for orcombined with hue and/or intensity values in transmitting underlyinginformation. For example, pixel characteristics can include pixelsaturation and brightness, as additional or alternative pixelcharacteristics for rendering the cardio-vibrational image matrix.

In some embodiments, the cardio-vibrational image matrix is output as animage file for use by a clinician in monitoring a patient. The imagepresented to the clinician, for example at a display of a computingdevice, may be a static image such as a static cardio-vibrational imagematrix graph. In another example, the clinician is presented with adynamic cardio-vibrational image, such as a moving image (or video)providing real-time or near-real-time feedback on the cardio-vibrationalsignals obtained through monitoring the patient. For example, the movingimage can move along a horizontal timeline in accordance with apredetermined or a user-configurable speed. For example, the movingimage can move at a rate of acquired real time data, but provide a useran ability to accelerate or slow down the moving image.

In an example, a dynamic ECG image (or video) can periodically refreshin accordance with a predetermined or a user-configurable refresh rate.For example, such a dynamic image can be refreshed at a periodic rateset by the user through a user-configurable parameter. For instance, therefresh rate may be set to be 5 seconds, and a user can modify therefresh rate to 30 seconds, 1 minute, 5 minutes, or more. In thisexample, the image can be refreshed as additional cardio-vibrationalsignal information is available. For instance, if the cardio-vibrationalinformation is acquired in real time, the image can be refreshed everypredefined period. For example, such predefined period can be 30seconds, 45 seconds, 90 seconds, 120 seconds, 180 seconds, 300 seconds,or other values therebetween.

In examples, an ECG image matrix can be derived from ECG signals. TheECG signals may be obtained from one or more ECG electrodes of a medicaldevice, such as a wearable heart monitoring device. The ECG image matrixvisually represents differences in timing and/or intensity of ECGfiducial points, such as P, Q, R, S, T, U, V values, and/or other ECGparameters and characteristics, such as QP, QR, ST, TU segment changes,among others. The ECG image matrix format is advantageous in providing acomparison mechanism that easily highlights to an end user substantialchanges in timing and/or intensity of ECG fiducial points, such as P, Q,R, S, T, U, V values, and/or other ECG parameters and characteristics,such as QP, QR, ST, TU segment changes, among others. For example, suchinformation contained in ECG signals of a predetermined duration can bedepicted in the ECG image matrix. For instance, such predeterminedduration can be of 30 seconds, 60 seconds, 90 seconds, 180 seconds, 300seconds, or values therebetween, In some examples, to transform the ECGmeasurements into an ECG image matrix, ECG measurements of apredetermined duration are generated from the ECG signals. The ECGmeasurements may be segmented into adjacent cardiac portions. Theparameter values of the ECG measurements of each cardiac portion may bemapped to pixel characteristic values (e.g., hue, intensity) and plottedas an ECG image matrix having, on one axis, a time progression of theadjacent cardiac portions and, on the other axis, the pixelcharacteristic values of each portion. While references herein to pixelcharacteristics values pertain to hue and/or intensity values, it isappreciated that other pixel characteristics may be substituted for orcombined with hue and/or intensity values in transmitting underlyinginformation. For example, pixel characteristics can include pixelsaturation and brightness, as additional or alternative pixelcharacteristics for rendering the ECG image matrix.

In some embodiments, the ECG image matrix is output as an image file foruse by a clinician in monitoring a patient. The image presented to theclinician, for example at a display of a computing device, may be astatic image such as a static ECG image matrix graph. In anotherexample, the clinician is presented with a dynamic ECG image, such as amoving image (or video) providing real-time or near-real-time feedbackon the ECG signals obtained through monitoring the patient. For example,the moving image can move along a horizontal timeline in accordance witha predetermined or a user-configurable speed. For example, the movingimage can move at a rate of acquired real time data, but provide a useran ability to accelerate or slow down the moving image.

In an example, a dynamic ECG image (or video) can periodically refreshin accordance with a predetermined or a user-configurable refresh rate.For example, such a dynamic image can be refreshed at a periodic rateset by the user through a user-configurable parameter. For instance, therefresh rate may be set to be 5 seconds, and a user can modify therefresh rate to 30 seconds, 1 minute, 5 minutes, or more. In thisexample, the image can be refreshed as additional ECG signal informationis available. For instance, if the ECG information is acquired in realtime, the image can be refreshed every predefined period. For example,such predefined period can be 30 seconds, 45 seconds, 90 seconds, 120seconds, 180 seconds, 300 seconds, or other values therebetween.

In some embodiments, the cardio-vibrational image matrix and/or an ECGimage matrix is developed in real-time to automatically monitor apatient for signs of an arrhythmia condition. For example, rather thanor in addition to being presented to a clinician, cardio-vibrationalimage matrices and/or ECG image matrices may be provided to an imageanalysis algorithm for automatically identifying an arrhythmia conditionoccurring in the patient. The image analysis algorithm may be a machinelearning algorithm applying one or more machine learning classifierseach trained to detect a different type of arrhythmia. In oneimplementation, the image analysis algorithm may be configured toidentify a state of arrhythmia versus a state of no arrhythmia. Further,in some implementations, the image analysis algorithm may be configuredto identify a type of arrhythmia. Advantageously, the cardio-vibrationalimage matrix analysis may be performed to confirm the presence of anarrhythmia as compared to noise within the signal, thus avoidingunnecessary delivery of electrical therapeutic therapy in the absence ofarrhythmia.

The cardio-vibrational image matrix, in some embodiments, is developedto automatically screen, using one or more machine learning classifiers,a patient’s cardiac signals for one or more cardiac risk biomarkers. Insome embodiments, the cardio-vibrational image matrix is developed as anumber of cardio-vibrational image matrices produced over time areanalyzed to identify trends in a patient’s cardiac risk or heart failurecondition. Alternatively, the ECG image matrix, in some embodiments, isdeveloped to be used in conjunction with the cardio-vibrational imagematrix to automatically screen, using one or more machine learningclassifiers, a patient’s cardiac (ECG and cardio-vibration) signals forone or more cardiac risk biomarkers.

In one aspect, the present disclosure relates to automatically screeninga cardio-vibrational image matrix and/or an ECG image matrix, usingmachine learning classifiers, to identify one or more cardiac riskbiomarkers. The automated screening, for example, may be used to monitorpatient health, determine a best course of treatment, and/or manageappropriate follow up care for a patient based upon a prediction of riskof future cardiac disease or disorder. The screening may take intoconsideration at least a portion of a patient’s ECG signals and/orcardio-vibrational signals captured over an extended period of time,such as at least ten minutes, at least one hour, about three hours,around one day, or up to a week. The cardiac risk biomarkers, forexample, may be representative of risk of one or more of sudden cardiacarrest (SCA), low ejection fraction (EF), or a stage of heart failure.The cardiac risk biomarkers, in some examples, may include EMAT, LVST,S3 intensity, and/or S3 width. One or more machine learning analysisprocesses, for example, may apply the machine learning classifiers tothe ECG image matrix and/or cardio-vibrational image matrix to determinea heart risk classification. The heart risk classification may beanalyzed, in light of additional metrics and/or patient factors, todetermine a set of heart risk metrics. The heart risk metrics may bepresented in a report, for example for review by a clinician or patient.

In one aspect, the present disclosure relates to analyzing a number ofcardio-vibrational image matrices and/or ECG image matrices producedover time to identify trends in a progression of a patient’s cardiachealth or heart failure condition. The heart failure progressionscreening, for example, may be used to monitor patient health, determinewhether a course of treatment appears to be successful in mitigatingworsening of heart failure, and/or manage appropriate follow up care fora patient based upon a present assessment of heart failure trends in thepatient. For example, the image matrices may be analyzed using machinelearning classifiers to determine a present stage of heart failure outof a number of stages of heart failure, such as the New York HeartAssociation (NYHA) heart failure classifications ranging from Class I toClass IV heart failure. For example, the output from such analysis mayclassify patients in accordance with the following Table 1.

TABLE 1 NYHA Class Patients with Cardiac Disease (Description of HFRelated Symptoms) Class I (Mild) Patients with cardiac disease butwithout resulting in limitation of physical activity. Ordinary physicalactivity does not cause undue fatigue, palpitation (rapid or poundingheart beat), dyspnea (shortness of breath), or anginal pain (chestpain). Class II (Mild) Patients with cardiac disease resulting in slightlimitation of physical activity. They are comfortable at rest. Ordinaryphysical activity results in fatigue, palpitation, dyspnea, or anginalpain Class III (Moderate) Patients with cardiac disease resulting inmarked limitation of physical activity. They are comfortable at rest.Less than ordinary activity causes fatigue, palpitation, dyspnea, oranginal pain. Class IV (Severe) Patients with cardiac disease resultingin the inability to carry on any physical activity without discomfort.Symptoms of heart failure or the anginal syndrome may be present even atrest. If any physical activity is undertaken, discomfort is increased.

In examples, the output of the machine learning classification canresult in a classification in accordance with the American HeartAssociation (AHA) heart failure stages. For example, the classificationcan be based as shown below.

-   Stage A: Presence of heart failure risk factors but no heart disease    and no symptoms-   Stage B: Heart disease is present but there are no symptoms    (structural changes in heart before symptoms occur)-   Stage C: Structural heart disease is present AND symptoms have    occurred-   Stage D: Presence of advanced heart disease with continued heart    failure symptoms requiring aggressive medical therapy

In accordance with the above classification, the machine learning modelscan be trained using thousands or more of training image matrices (alongwith other cardiac and health history information of patients) toproduce the relevant heart failure classification for the patient. Aprogression of heart failure from Class 1 to Class 2, and then on toClass 3 or Class 4, or a progression of heart failure from Stage A toStage B, and then on to Stage C or Stage D indicates worsening heartfailure. For example, improvements in NYHA or AHA class symptomsindicate improving heart failure condition in the patient. In examples,instead of or in addition to NYHA or AHA class symptoms, the device canmonitor predetermined physiologic parameters to determine improving orworsening heart failure condition in the patient. For example, thedevice can monitor for trends in certain cardiac risk biomarkersincluding increase of decrease in EMAT, increase or decrease in S3intensity, increase or decrease in ejection fraction (EF) values,increase or decrease in pulmonary capillary wedge pressure, or increaseor decrease in cardiac output. Such changes in these cardiac riskbiomarkers are used by machine learning classifiers in outputting theappropriate status of the patient and further, in some examples,recommending the appropriate heart failure treatment for the patient. Inimplementations, the machine learning models used in the classificationmay be trained in part using historic image matrices generated fromsignals produced from monitoring the patient, thereby advantageouslytaking into consideration the unique cardiac signature of the patient.The classification provided by the machine learning analysis process(es)may be further analyzed, in view of additional historic sets of ECGand/or cardio-vibrational metrics, to determine heart failureprogression metrics. The heart failure progression metrics may bepresented in a report, for example for review by a clinician or patient.

FIG. 1 is a flow chart of an example method 100 for generating acardio-vibrational image matrix from cardio-vibrational signals obtainedthrough monitoring a patient. The method 100, for example, may be usedin monitoring a cardiac condition of a patient using a wearable medicaldevice. In some examples, the method 100 may be performed by processingcircuitry of a medical device such as a wearable medical device, by oneor more processors of a server or server system, or by one or moreprocessors of a cloud computing platform. Portions of the method 100, insome embodiments, are performed on different computing platforms. Forexample, the generation of cardio-vibrational measurements (104) may beperformed locally at a medical device, while producing thecardio-vibrational image matrix (112) may occur at a remote computingdevice or system.

In some implementations, the method begins with obtaining, by avibrational sensor monitoring a patient’s heart, cardio-vibrationalsignals (102). The vibrational sensor, in some examples, may be amedical grade accelerometer or microphone configured to monitorpulmonary vibrations. The vibrational sensor, in one example, attachedto or built into a wearable cardiac monitoring device. The sensor may bepositioned to contact skin on the patient. In another example, thesensor is releasably attached to the patient, for example using amedical grade adhesive. The signals may be obtained by one or moreprocessors of a medical device, such as a wearable cardiac monitoringdevice. The signals, in another example, may be relayed via a network toa remote computing system such as a medical facility server or a cloudcomputing platform.

In some implementations, cardio-vibrational measurements are generatedfrom the cardio-vibrational signals (104). The cardio-vibrationalmeasurements, for example, may include parameter values corresponding toat least a number of peaks and troughs in the cardio-vibrational signal.The cardio-vibrational measurements, in some examples, may representwaveform morphology captured in the cardio-vibrational signal such as,in some examples, magnitudes, amplitudes, and/or phases captured in thecardio-vibrational measurements.

In some implementations, if an ECG reading corresponding to a sametimeframe as the capture of the cardio-vibrational signals is available(106), a time scale of the cardio-vibrational measurements is registeredwith one or more types of deflection features of the ECG reading (108).In some examples, the time scale of the cardio-vibrational measurementsmay be registered with R peaks, P peaks, T peaks, and/or QRS complexesof the ECG reading. Registering the cardio-vibrational signal with theECG reading, for example, may allow analysis of measurements obtainedusing both mechanisms, thereby supporting a more in-depth evaluation ofcardiac condition. Further, registering the ECG reading with thecardio-vibrational measurements may be advantageous in enabling greaterprecision in the calculation of certain metrics, such aselectromechanical activation time (EMAT) or left ventricular systolictime (LVST).

In some implementations, a set of the cardio-vibrational measurements isidentified for conversion into a cardio-vibrational image matrix (110).The set of the cardio-vibrational measurements may represent a selectedperiod of time (e.g., predetermined duration) such as, in some examples,15 seconds, 30 seconds, 45 seconds, or 60 seconds to be real-real-timeresponsive to cardiac conditions occurring in the heart of the patientsuch as arrhythmia, thereby supporting timely and responsive therapeuticsupport, such as defibrillation or pacing pulse delivery. In furtherexamples, the selected period of time may be around 30 seconds, around60 seconds, around 90 seconds, around 120 seconds, around 180 seconds,around 300 seconds, or around 5 minutes to allow for analysis of lessfrequent cardiac behaviors and/or longer signatures of activity. Inadditional examples, the selected period of time may be at least 10minutes for detailed analysis of heart failure and cardiac riskconditions, such as ejection fraction (EF) analysis or supraventriculartachycardia (SVT) classification. The predetermined duration, in furtherexamples, may be between around 15 seconds and around 30 seconds,between around 30 seconds and around 45 seconds, between around 45seconds and around 60 seconds, between around 60 seconds and around 90seconds, between around 90 seconds and 120 seconds, between around 2minutes or around 3 minutes, or between around 3 minutes and around 10minutes.

In some embodiments, the set of cardio-vibrational measurements isselected so that the signals begin and/or end based upon the timeregistration with the ECG reading if registering was conducted. Forexample, the set of the cardio-vibrational measurements may beconfigured to begin at a certain type of ECG peak or at a set timeoffset from the type of ECG peak. The set may be further selected torepresent at least a threshold number of a certain type of peak, suchas, in an illustrative example, 60 R-peaks.

The identified set of cardio-vibrational measurements, in someimplementations, is segmented into the adjacent cardiac portions (110).Each cardiac portion, in some embodiments, represents a duration smallerthan the predetermined duration such as, in some examples, around 100milliseconds, around 1000 milliseconds, around 1 second, around 2seconds, around 5 seconds, around 10 seconds, around 15 seconds, oraround 20 seconds. The duration, in some embodiments involving timeregistration with the ECG reading, is selected to capture, in eachcardiac portion, a threshold number of a type of ECG peak. For example,such threshold number can be at least 2, 3, 4 or 5 of a type of ECGfiducial point. For example, the threshold number may be at least 2 Rpeaks of the ECG signal. In illustration, turning to FIG. 2A, thecardiac portions may be divided so that the R peak registration alignswith around 0.2 seconds on a y-axis 202 b (e.g., over a span of 1.2second), such that cardio-vibrational measurements corresponding to avalve opening is anticipated shortly thereafter.

The alignment with the R peak registration (or, conversely, anotherdeflection feature), for example, may compensate for variations induration and amplitude of individual cardio vibrations during eachcardiac cycle. The cardiac portions, in further examples, may generallyeach have a duration spanning between around 1 millisecond and around100 milliseconds, between 100 milliseconds and around 1000 milliseconds,between around 1 second and around 2 seconds, between around 2 secondsor around 5 seconds, or between around 5 seconds and around 10 seconds.The period of time represented by each cardiac portion, in someimplementations, may be selected in part based upon a type of analysisdesired. For example, real-time arrhythmia monitoring may involve ashorter timeframe, while sudden cardiac arrest (SCA) risk predictionanalysis may involve review of lengthier timeframes. The duration, forexample, may correspond in part to a total duration of the set (e.g., alonger set of cardio-vibrational measurements may correspond to a longertimeframe for each cardiac portion of the set of cardio-vibrationalmeasurements).

In some implementations, a cardio-vibrational image matrix is producedby plotting the adjacent cardiac portions using pixel characteristicvalues mapped to parameter values of the correspondingcardio-vibrational measurements (112). The pixel characteristic values,for example, may include pixel hue and/or pixel intensity. In anexample, the color spectrum of the pixel characteristic values mayinclude at least three colors, four colors, eight colors, or up to 16colors. For example, as illustrated in FIG. 2A, an examplecardio-vibrational image matrix 200 includes hues of yellow, orange,red, and green pixels. The pixel characteristic values, for example, maybe arranged in a heat map to draw a clinician’s attention toparticularly relevant or important data, such as occurrences andintensities of S1, S2, S3, and S4 peaks. As illustrated in FIG. 2B, apixel heat map scale 216 of an example cardio-vibrational image matrix210 illustrates a range of magnitude intensities ranging from white tored. In another example, the pixel characteristic values may be mappedto distinct hues and/or intensities beneficial for automated analysis.The map of pixel characteristic values, in some embodiments, is selectedfrom a set of maps of pixel characteristic values designed for differentcategories of parameter values, different types of interpretation,and/or highlighting different types of information. For example, a firstmap of pixel characteristic values may be applied in mapping amplitudeparameter values, a second map may be applied in mapping phase parametervalues, and a third map may be applied in mapping magnitude parametervalues. Plotting, for example, may involve creating a graphicrepresentation of the mapped parameter values as a time progression ofadjacent cardiac portions plotted along a first axis of thecardio-vibrational image matrix, where parameter values of individualcardiac portions are plotted along a second axis of thecardio-vibrational image matrix. Turning to FIG. 2A, for example, theexample cardio-vibrational image matrix 200 includes cardiac portionsspanning 1.2 second each along a y-axis 202 b over a one-minute durationof cardio-vibrational measurements along an x-axis 202 a. In FIG. 2B,the example cardio-vibrational image matrix 210 includes cardiacportions spanning 1.2 seconds each along a y-axis 212 over a 30 secondduration (21.0 minutes to 21.5 minutes) of cardio-vibrationalmeasurements plotted along an x-axis 214.

In some implementations, if enhancement of the cardio-vibrational imagematrix is desired (114), a filtering or smoothing algorithm is appliedto at least certain pixel characteristic values in thecardio-vibrational image matrix (116). In some embodiments, a smoothingalgorithm is applied to proximately located pixel characteristic valuesin the cardio-vibrational image matrix. The smoothing algorithm mayreduce noise across the data by adjusting proximate pixel groups todisplay an averaging of the data represented by the pixel group (e.g., arectangular section including at least 4 pixels). The smoothingalgorithm, for example, may improve human readability of the data,leading to more accurate interpretation of the image data. The smoothingdata may advantageously remove extraneous noise from the image, allowinga human to swiftly recognize important trends and anomalies within thecardio-vibrational image matrix. As illustrated in FIG. 2A, for example,the cardio-vibrational image matrix has been enhanced using a smoothingalgorithm. FIG. 2B, on the other hand, has not been enhanced withsmoothing to illustrate, in comparison, the effect of a smoothingalgorithm.

In implementations, one or more template cardio-vibrational and/or ECGimage matrices can be recorded and stored for analysis and/orcomparison. For instance, an initial baseline cardio-vibrational and/orECG image matrix can be recorded to be used as a template. For example,the initial baseline can be recorded at a predetermined time (e.g.,prior to use of the device, such as during an initial fitting).Thereafter, new cardio-vibrational and/or ECG image matrices may becompared to the patient’s baseline template. In examples, the baselinetemplate can be periodically updated. For example, the patient can beprompted on a recurring period to manually record a baseline templatecardio-vibrational and/or ECG image matrix. For example, the device canautomatically initiate on a recurring period recording a baselinetemplate cardio-vibrational and/or ECG image matrix. For example, thedevice can initiate a new baseline recording when a threshold number offalse positives (e.g., 3-4 false positives in a 48-hour period, or 8-10false positives in a one-week period) is recorded. In implementations,the device can cause a baseline template to record the patient’sdominant rhythm and/or any abnormal rhythms that the patient may havehad in the past. The device can use the prior knowledge of storedabnormal rhythms to avoid false alerts or otherwise reducing a number ofalerts to the patient. In the example of the abnormal rhythms, when sucha rhythm occurs, but the patient is otherwise physiologically normal(e.g., the patient presses response buttons on a wearable defibrillatoror otherwise reports they are feeling fine via a user interface), thedevice can record the abnormal rhythm to store as a template for futurecomparison.

In examples, the device can have access to a universal rhythm templatelibrary based on templates recorded across several tens, hundreds, orthousands of similarly situated patients (e.g., similar patientdemographics as the subject patient). For example, the demographics caninclude patients having similar age, ethnicity, weight, height, priorcardiac history, prior treatment history, among others. In this manner,an individual patient’s rhythm as indicated in a cardio-vibrational orECG image matrix can be rapidly compared via techniques described hereinto assess a quick and accurate diagnosis. For example, such a templatelibrary can be stored on the wearable device or a gateway device withwhich the wearable device is in communication (e.g., wirelesscommunication) via Bluetooth®, Zigbee^(®), or WiFi^(®) technology. Insome implementations, the cardio-vibrational image matrix is output asan image file (118). The cardio-vibrational image matrix, for example,may be provided to a display of a computing device for review by aclinician or incorporated in a report for review by an end user. Forexample, the image may be displayed via a user interface mounted on anexternal housing of the wearable medical device. In implementations, theimage may be transmitted via a network interface in the wearable medicaldevice (described in further detail below) to a remote server, fordisplay at a remote computer screen viewed by a technician, a caregiver(e.g., a nurse, a physician, a physician’s assistant, or otherauthorized medical representative) or other authorized person, incommunication with the remote server. The cardio-vibrational imagematrix, in another example, may be provided to one or more analysisalgorithms for automated analysis of the data captured in thecardio-vibrational image matrix. If the cardio-vibrational image matrixwas time registered to an ECG reading, in some embodiments, thecardio-vibrational image matrix is output to a graphic preparationalgorithm for combining the cardio-vibrational image matrix with ECGdata in a manner that enhances review of the information by an end user.In some embodiments, outputting the cardio-vibrational image matrixinvolves transmitting the cardio-vibrational image matrix to a remotecomputing system via a network. As noted, the end user may be thepatient or a designated representative of the patient (e.g., a healthcare proxy), a caregiver (e.g., a nurse, a physician, a physician’sassistant, or other authorized medical representative), or otherauthorized person (e.g., a service technician).

In some implementations, if additional sets of cardio-vibrationalmeasurements are available for transformation (120), the method 100continues with segmenting a next set of cardio-vibrational measurementsinto adjacent cardiac portions (110). The time-series ofcardio-vibrational image matrices generated by iterating through atleast the portion of the method 100, for example, may support real-timedata interpretation and/or a dynamic (e.g., video) output to a cliniciandevice.

Although described as a series of steps, in other embodiments, themethod 100 may include more or fewer steps. For example, the method 100may include obtaining ECG signals and generating ECG measurements forregistering the time scale (108). In another example, rather thanoutputting the cardio-vibrational image matrix as an image file, thecardio-vibrational image matrix may be added to an existing file or theplotted data streamed to a remote processor for additional operations.In some implementations, a portion of the steps of the method 100 may beperformed in parallel. For example, while the cardio-vibrationalmeasurements are being transformed into the cardio-vibrational imagematrix through segmenting (110) and plotting (112), a next set ofcardio-vibrational signals may be obtained (102) and used to generate anext set of cardio-vibrational measurements (104). Other modificationsto the method 100 are possible while remaining in the scope and spiritof the disclosure.

FIGS. 3A and 3B illustrate a flow chart of an example method 300 forgenerating and applying an ECG image matrix for monitoring a cardiaccondition of a patient. The method 300, for example, may be used inmonitoring a cardiac condition of a patient using a wearable medicaldevice. In some examples, the method 300 may be performed by processingcircuitry of a medical device such as a wearable medical device, by oneor more processors of a server or server system, or by one or moreprocessors of a cloud computing platform. Portions of the method 300, insome embodiments, are performed on different computing platforms. Forexample, the generation of ECG measurements (304) may be performedlocally at a medical device, while producing the ECG image matrix(310-314) may occur at a remote computing device or system.

In some implementations, the method 300 begins with obtaining ECGsignals from a wearable medical device (302). The ECG signals may beobtained from skin-facing ECG electrodes integrated into or connected toa wearable cardiac monitoring device, a cardiac holter monitor, or acardiac monitoring and treatment device (e.g., with an automatedexternal defibrillator or a wearable cardioverter defibrillator). Thesignals may be obtained by one or more processors of the wearablemedical device. The signals, in another example, may be relayed via anetwork to a remote computing system such as a medical facility serveror a cloud computing platform.

In some implementations, ECG measurements are generated from the ECGsignals (304). The ECG measurements, for example, may include a varietyof ECG features such as, in some examples, a set of R peaks, a set of Ppeaks, a set of T peaks, or a set of QRS complexes.

In some implementations, if cardio-vibrational measurements areavailable (306), a time scale of the cardio-vibrational measurements isregistered with deflection features of the ECG measurements (308). Insome examples, the time scale of the cardio-vibrational measurements maybe registered with R peaks, P peaks, T peaks, and/or QRS complexes ofthe ECG reading. Registering the cardio-vibrational signal with the ECGreading, for example, may allow analysis of measurements obtained usingboth mechanisms, thereby supporting a more in-depth evaluation ofcardiac condition. Further, registering the ECG reading with thecardio-vibrational measurements may be advantageous in enabling greaterprecision in the calculation of certain metrics, such aselectromechanical activation time (EMAT) or left ventricular systolictime (LVST). In examples, a temporal component will also be used todetermine if the ECG readings are changing over seconds, or minutes, orremaining stable.

In some implementations, the cardio-vibrational measurements aretransformed into a cardio-vibrational image matrix (310). Thecardio-vibrational measurements, for example, may be transformed into acardio-vibrational image matrix as described in steps 110-116 of FIG. 1.

In some implementations, the ECG measurements are segmented intoadjacent ECG portions (312). The set of the ECG measurements used in thetransformation may represent a selected period of time (e.g.,predetermined duration) such as, in some examples, 15 seconds, 30seconds, or 45 seconds to be real-real-time responsive to cardiacconditions occurring in the heart of the patient such as arrhythmia,thereby supporting timely and responsive therapeutic support, such asdefibrillation shock or pacing pulse delivery. In further examples, theselected period of time may be around 90 seconds, around 120 seconds, oraround 2 or 3 minutes to allow for analysis of less frequent cardiacbehaviors and/or longer periods of arrhythmic activity. In additionalexamples, the selected period of time may be at least 10 minutes fordetailed analysis of heart failure and cardiac risk conditions, such asejection fraction (EF) analysis or supraventricular tachycardia (SVT)classification. The predetermined duration, in further examples, may bebetween around 15 seconds and around 30 seconds, between around 30seconds and around 45 seconds, between around 45 seconds and around 60seconds, between around 60 seconds and around 90 seconds, between around90 seconds and 120 seconds, between around 2 minutes or around 3minutes, or between around 3 minutes and around 10 minutes. In examples,selection of the predetermined duration or the duration of the adjacentportions, e.g., adjacent ECG portions 312 can be automatically set basedon self-learning of such durations for individual patients. For example,the medical device may automatically determine an appropriate value forthe predetermined duration or the duration of the adjacent portions froman analysis of prior cardio-vibrational and/or ECG image matrices forthe patient. In an illustration, if the device had previously assessedcardio-vibrational and/or ECG image matrices based on 45 seconddurations (x-axis), with 1 second segments (y-axis), and suchassessments were used in prior machine-learning based classifications,then for ongoing and/or new classifications similar duration parameterscan be used.

The identified set of ECG measurements, in some implementations, issegmented into the adjacent ECG portions (312). Each ECG portion, insome embodiments, represents a duration smaller than the predeterminedduration such as, in some examples, around 100 milliseconds, around 1000milliseconds, around 1 second, around 2 seconds, around 5 seconds, oraround 10 seconds. The duration, in some embodiments, is selected tocapture, in each ECG portion, a threshold number of deflection features,such as, in some examples, at least two R peaks of the ECG reading. TheECG portions, in further examples, may generally each have a durationspanning between around 1 millisecond and around 100 milliseconds,between 100 milliseconds and around 1000 milliseconds, between around 1second and around 2 seconds, between around 2 seconds or around 5seconds, or between around 5 seconds and around 10 seconds. The periodof time represented by each ECG portion, in some implementations, may beselected in part based upon a type of analysis desired. For example,real-time arrhythmia monitoring may involve a shorter timeframe, whilesudden cardiac arrest (SCA) risk prediction analysis may involve reviewof lengthier timeframes. The duration, for example, may correspond inpart to a total duration of the set (e.g., a longer set of ECGmeasurements may correspond to a longer timeframe for each ECG portionof the set of ECG measurements). In examples, selection of thepredetermined duration or the duration of the adjacent portions, e.g.,adjacent ECG portions 312 can be automatically set based onself-learning of such durations for individual patients. For example,the medical device may automatically determine an appropriate value forthe predetermined duration or the duration of the adjacent portions froman analysis of prior cardio-vibrational and/or ECG image matrices forthe patient. In an illustration, if the device had previously assessedcardio-vibrational and/or ECG image matrices based on 45 seconddurations (x-axis), with 1 second segments (y-axis), and suchassessments were used in prior machine-learning based classifications,then for ongoing and/or new classifications similar duration parameterscan be used.

In some implementations, the ECG portions are plotted using pixelcharacteristic values mapped to parameter values of corresponding ECGmeasurements to produce an ECG image matrix (314). The pixelcharacteristic values, for example, may include pixel hue and/or pixelintensity. In an example, the color spectrum of the pixel characteristicvalues may include at least three colors, four colors, eight colors, orup to 16 colors. For example, as illustrated in FIG. 4 , an example ECGimage matrix 402 includes hues of yellow, orange, red, green, and bluepixels.

The pixel characteristic values, for example, may be arranged in a heatmap to draw a clinician’s attention to particularly relevant orimportant data, such as R peaks, P peaks, T peaks, and QRS complexes. Asillustrated in FIG. 4 , a pixel heat map scale 404 of the example ECGimage matrix 402 illustrates a range of magnitude intensities rangingfrom blue to red. In another example, the pixel characteristic valuesmay be mapped to distinct hues and/or intensities beneficial forautomated analysis. The map of pixel characteristic values, in someembodiments, is selected from a set of maps of pixel characteristicvalues designed for different categories of parameter values, differenttypes of interpretation, and/or highlighting different types ofinformation. Plotting, for example, may involve creating a graphicrepresentation of the mapped parameter values as a time progression ofadjacent ECG portions plotted along a first axis of the ECG imagematrix, where parameter values of individual ECG portions are plottedalong a second axis of the ECG image matrix. Turning to FIG. 4 , forexample, the example ECG image matrix 402 includes ECG portions spanning1 second each along a y-axis 406 over a 30 second duration of ECGmeasurements along an x-axis 408.

Returning to FIG. 3A, in some implementations, if a cardio-vibrationalimage matrix was generated (316), the ECG image matrix and thecardio-vibrational image matrix are applied to one or more machinelearning classifiers to monitor a cardiac condition in the patient(318). If, instead, only an ECG image matrix was generated (316), insome implementations, the ECG matrix is applied to one or more machinelearning classifiers to monitor a cardiac condition in the patient(320). The cardiac condition, for example, may include an arrhythmiacondition. In some implementations, the ECG (and, optionally, thecardio-vibrational image matrix) is applied to multiple machine learningclassifiers in parallel to screen for a number of potential cardiacconditions. In other implementations, the ECG image matrix (and,optionally, the cardio-vibrational image matrix) may be applied to afirst machine learning classifier (e.g., to separate instances of noisefrom instances of an abnormality) then subsequently to one or moreadditional machine learning classifiers either serially or in parallel.Certain classifiers may be trained to apply only one of the two imagematrices, while other machine learning classifiers are trained to applyboth the ECG image matrix and the cardio-vibrational image matrix.

Turning to FIG. 3B, in some implementations, if an arrhythmia conditionis detected (322) by the one or more machine learning classifiers, atype of arrhythmia is determined (324). For example, a first machinelearning classifier may detect possibility of an arrhythmia (322) (e.g.,abnormality). Responsive to the detection, one or more additionalmachine learning classifiers may be applied to determine a type of thearrhythmia (324). In a first example, the first machine learningclassifier may detect possibility of an arrhythmia, while the secondmachine learning classifier determines whether the ECG measurements(and, optionally cardio-vibrational measurements) comprises indicationof noise rather than presence of an arrhythmia. Thus, in a simplestform, determining the type of arrhythmia may correspond to determiningpresence of arrhythmia or a determination that the signals representnoise. In another example, machine learning classifiers may be providedto identify multiple types of arrhythmia, including, in some examples,supraventricular tachycardia (SVT), ventricular tachycardia, ventricularfibrillation, tachycardia, bradycardia, asystole, a heart pausecondition, pulseless electrical activity, or atrial fibrillation.

In some implementations, if the type of arrhythmia corresponds to anerratic heart rate condition, tachycardia, bradycardia, asystole orpulseless electrical activity (PEA) (326), the wearable medical deviceis caused to is caused to select an appropriate pacing routinecorresponding to the type of arrhythmia, culminating in delivery ofpacing pulses (328). The medical device, for example, may be a wearabledefibrillation device such as the device 1000 described below inrelation to FIG. 10 . In another example, the device may be a separatedefibrillation device in communication with processing circuitryperforming the method 300. In some implementations, after causing thewearable medical device to deliver the one or more pacing pulses, themethod 300 then returns to obtaining ECG signals (302).

Where bradycardia is detected and the intrinsic cardiac rate of thepatient is below that of a hysteresis rate of the patient, the medicaldevice can be configured to pace the patient at a pre-set base pacingrate. During this time, the device will continue to monitor thepatient’s intrinsic heart rate and will withhold pacing pulses in theevent that an intrinsic heart beat is detected within designatedinterval corresponding to the hysteresis rate, resulting in an on-demandpacing provided as maintenance pacing.

For responding to tachycardia, the medical device may additionallyinclude another pacing rate, an anti-tachyarrhythmia pacing to rate,above which the device will identify that the patient is suffering fromtachycardia, and will pace the patient in a manner to bring thepatient’s intrinsic heart back toward the base racing rate. For example,the device may employ a technique known as overdrive pacing wherein aseries of pacing pulses (e.g., between about 5 and 10 pacing pulses) aredelivered to the patient at a frequency above the intrinsic rate of thepatient in an effort to gain control of the patient’s heart rate. Onceit is determined that the device is in control of the patient’s heartrate, the rate (i.e., the frequency) of the pulses may be decremented,for example by about 10 ms, and another series of pacing pulsesdelivered. This delivery of pulses and the decrease in frequency maycontinue until the detected intrinsic cardiac rate of the patient isbelow the anti-tachyarrhythmia pacing rate, or at the base pacing rate.

For an erratic heart rate, the medical device may perform a type ofpacing that is similar to a combination of maintenance pacing andoverdrive pacing discussed above. For example, where the medicalmonitoring and treatment device detects an erratic heart rate with nodiscernable sinus rhythm, the device may deliver a series of pacingpulses (e.g., between about 5 and 10 pacing pulses) to the patient at aparticular frequency. This frequency may be one that is above a lowerfrequency of a series of detected intrinsic beats of the patient’s heartand below an upper frequency of the detected intrinsic beats of thepatient’s heart. After delivering the series of pulses, the device maymonitor the patient’s heart to determine if it has synchronized to therate of the series of delivered pulses. Where the intrinsic rate of thepatient’s heart is still erratic, the device may increase the frequencyof the series of pulses and deliver another series. This may continueuntil it is established that the patient’s heart is now in a moreregular state. Upon determining that the patient’s heart is now in amore regular state, the device may perform maintenance pacing if it isdetermined that the patient’s intrinsic heart rate is too low asdescribed above, or perform pacing at a decremented rate, if such iswarranted.

For responding to asystole or a detected condition of pulselesselectrical activity, the medical monitoring and treatment device mayperform maintenance pacing. This type of pacing would be performed aftera series of one or more defibrillating shocks that attempt to restore anormal sinus rhythm to the heart of the patient.

In implementations, the medical device may be configured to perform aparticular type of pacing only after a programmable delay after suchcardiac arrhythmias are detected, or after a programmable period of timeafter one or more defibrillating shocks are delivered.

In some implementations, if the type of arrhythmia corresponds to aventricular fibrillation or supraventricular tachycardia (330), thewearable medical device is caused to charge up the energy storagedevices (e.g., capacitors) and deliver a defibrillation or cardioversionshock to the patient (332). The medical device, for example, may be awearable therapeutic shock device such as the device 1000A describedbelow in relation to FIG. 10A. In another example, the device may be aseparate therapeutic shock device in communication with processingcircuitry performing the method 300. In some implementations, aftercausing the wearable medical device to deliver the cardioversion ordefibrillation shock, the method 300 returns to obtaining ECG signals(302).

If, instead, the type of arrhythmia is a heart pause condition,pulseless electrical activity, atrial fibrillation, or simply noisewithin the signal, in some implementations, the method 300 stores aportion of the ECG and/or cardio-vibrational signal in memory of themedical device, and returns to obtaining ECG signals (302). Inimplementations, the stored portion of the ECG and/or cardio-vibrationalsignal can be transmitted to a remote server for analysis and/or displayvia a viewing terminal to a caregiver, technician, or other authorizedperson.

Although described as a particular series of operations, in otherimplementations, the method 300 includes more or fewer steps. Forexample, rather than being a separate arrhythmia type determination step(324), in other embodiments, applying the ECG image matrix (318 or 320)includes detecting a condition such as an arrhythmia type. Further, insome embodiments, certain steps of the method 300 may be performed in adifferent order or simultaneously. For example, while the ECGmeasurements (and cardio-vibrational measurements) are being generatedand transformed into the ECG image matrix (and cardio-vibrational imagematrix) (304-314), previously created image matrices may be applied tothe machine learning classifiers (318 or 320), and next set of ECGsignals may be obtained (302). Other modifications are possible whileremaining within the scope and the spirit of the method 300.

FIG. 5 is a flow diagram of an example process 500 for collectingsignals from a wearable medical device 502 and forming both imagematrices and supporting metrics for monitoring a cardiac condition in apatient. The process 500, in some examples, may be performed byprocessing circuitry of the medical device 502 such as a wearablemedical device, by one or more processors of a server or server system,or by one or more processors of a cloud computing platform. Portions ofthe process 500, in some embodiments, are performed on differentcomputing platforms. The process 500 includes a number of engines. Eachengine may represent one or more software algorithms,computer-implemented functions, and/or hardware logic routines. Eachengine may be configured to be executed as processing commands executedon processing circuitry and/or routines implemented as a specializedcircuit design, such as a programmable logic chip design.

In some implementations, the process 500 begins with obtaining ECGsignals 506 from an ECG monitoring unit 504 and cardio-vibrationalsignals 516 from a vibrational sensor 514 of a medical device 502. Themedical device 502, for example, may be one of the medical devices1100A-1100D described below in relation to FIGS. 11A-11D. The ECGsignals 506, for example, may be obtained by ECG electrodes integratedinto or connected to a cardiac monitoring device, as described inrelation to step 302 of the method 300 of FIG. 3A. The ECG electrodes,for example, may be the electrodes 1022 described in relation to FIG. 10or electrodes 1112 described in relation to FIGS. 11A-11D. Thecardio-vibrational signals 516, for example, may be obtained by one ormore vibrational sensors monitoring a patient’s heart, as described, forexample, in relation to step 102 of the method 100 of FIG. 1 . The oneor more vibrational sensors may include the sensors 1024, 1026, and/or1030 described in greater detail below in relation to FIG. 10 .

In some implementations, the ECG signals 506 are provided to an ECGmeasurement generation engine 508 to generate ECG measurements 510. TheECG measurements 510, for example, may be generated as described inrelation to step 304 of the method 300 of FIG. 3A.

In some implementations, the cardio-vibrational signals 516 are providedto a cardio-vibrational measurement generation engine 518 to generatecardio-vibrational measurements 520. The cardio-vibrational measurements520, for example, may be generated as described in relation to step 104of FIG. 1 .

In some implementations, the ECG measurements 510 and thecardio-vibrational measurements 520 are stored to a data repository 512by the ECG measurement generation engine 508 and the cardio-vibrationalmeasurement generation engine 518, respectively.

The ECG measurements 510 and the cardio-vibrational measurements 520, insome implementations, are accessed by a time scale registration engine522 to generate a set of registered ECG measurements 526 which are timeregistered to a set of registered cardio-vibrational measurements 528.The time scale registration engine 522, the registered measurements 526,528, for example, may be generated as described in step 108 of themethod 100 of FIG. 1 and/or step 308 of the method 300 of FIG. 3A. Thetime scale registration engine 522 may obtain one or more deflectionfeature selections 524 for registering the ECG measurements 510 with thecardio-vibrational measurements 520. The deflection feature selections524, in an illustrative example, may default to registering based on Rpeaks captured in the ECG measurements 510. This default registration,for example, may be selected in view of R peaks being typically readilyidentified within an ECG signal. In further examples, the deflectionfeature selections can include P peaks, T peaks, and/or QRS complexes ofthe ECG measurements 510.

In some implementations, the registered ECG measurements 526 and theregistered cardio-vibrational measurements 528 are provided to at leastone segmenting engine 530 to segment the registered measurements 526 and528 into ECG portions 532 and cardio-vibrational portions 542,respectively. The segmenting engine(s) 530, for example, may segment theregistered measurements 526 and 528 as described in relation to step 110of the method 100 of FIG. 1 and/or step 312 of the method 300 of FIG.3A.

In some implementations, the ECG portions 532 are provided to an ECGimage matrix generation engine 534 for generating an ECG image matrix538. The ECG image matrix 538 may be produced by the ECG image matrixgeneration engine 534, for example, as described in relation to step 314of the method 300 of FIG. 3A. The ECG image matrix generation engine 534may use a mapping of parameter values and pixel characteristic values536 for mapping ECG measurements within each of the ECG portions tocorresponding pixel characteristic values.

In some implementations, the ECG image matrix 538 is stored to an imagematrix repository. The ECG image matrix 538, for example, may be storedin a database structure identifying the source (e.g., patientidentifier), time stamp, and one or more links to corresponding ECGmeasurements 510 and/or registered ECG measurements 526. In anotherexample, a portion of this information, such as the time stamp and thepatient identifier, may be stored as meta data in the image filestructure. The ECG image matrix 538, in some embodiments, is stored in alossless image file structure such as, for example, a PNG file whichretains the original information accurately. In other embodiments, theECG image matrix 538 is stored as a lossy (e.g., compressed) image fileformat such as a JPG file, to reduce storage demands for retaininghistoric image matrix files.

In some implementations, the cardio-vibrational portions 542 areprovided to a cardio-vibrational image matrix generation engine 544 forgenerating a cardio-vibrational image matrix 548. The cardio-vibrationalimage matrix 548, for example, may be generated as described in relationto steps 112-116 of the method 100 of FIG. 1 and/or step 310 of themethod 300 FIG. 3A. The cardio-vibrational image matrix generationengine 544 may use a mapping of parameter values and pixelcharacteristic values 536 for mapping cardio-vibrational measurementswithin each of the cardio-vibrational portions to corresponding pixelcharacteristic values.

In some implementations, the cardio-vibrational image matrix 548 isstored to the image matrix repository 540. The cardio-vibrational imagematrix 548, for example, may be stored in a database structureidentifying the source (e.g., patient identifier), time stamp, and oneor more links to corresponding cardio-vibrational measurements 520and/or registered cardio-vibrational measurements 528. In anotherexample, a portion of this information, such as the time stamp and thepatient identifier, may be stored as meta data in the image filestructure. The cardio-vibrational image matrix 548, in some embodiments,is stored in a lossless image file structure such as, for example, a PNGfile which retains the original information accurately. In otherembodiments, the cardio-vibrational image matrix 548 is stored as alossy (e.g., compressed) image file format such as a JPG file, to reducestorage demands for retaining historic image matrix files.

In some implementations, the ECG measurements 510 and thecardio-vibrational measurements 520 are provided to one or more metricscalculating engines 550 to calculate ECG metrics 552 andcardio-vibrational metrics 554. The ECG metrics 522, in some examples,may include heart rate (such as average, median, mode, or otherstatistical measure of the heart rate, and/or maximum, minimum, resting,pre-exercise, and post-exercise heart rate values and/or ranges), heartrate variability metrics, PVC burden or counts, atrial fibrillationburden metrics, pauses, heart rate turbulence, QRS height, QRS width,changes in a size or a shape of morphology of the ECG information,cosine R-T, artificial pacing, QT interval, QT variability, T wavewidth, T wave alternans, T-wave variability, and/or ST segment changes.The cardio-vibrational metrics 554 may include, in some examples,cardio-vibrational signal values including any one or all of S1, S2, S3,and S4, electromechanical activation time (EMAT), average EMAT,percentage of EMAT (% EMAT), systolic dysfunction index (SDI), and/orleft ventricular systolic time (LVST).

In some implementations, the ECG metrics 552 and the cardio-vibrationalmetrics 554 are stored to a metrics repository 556. The ECG metrics 552and the cardio-vibrational metrics 554, for example, may be stored in adatabase structure identifying the source (e.g., patient identifier),time stamp, and one or more links to corresponding ECG measurements 510,cardio-vibrational measurements 520, registered ECG measurements 526,and/or registered cardio-vibrational measurements 528. The metricsrepository 556, the data repository 512, and the image matrix repository540, in some embodiments, are each configured as part of a database ofinformation generated using data supplied by sensors such as the ECGsensors 504 and the vibrational sensor(s) 514 of the medical device 502.

FIG. 8 is a flow diagram of an example process 800 for applying machinelearning classifiers to ECG image matrices and to cardio-vibrationalimage matrices to automatically determine whether to apply an electricaltherapeutic shock to a patient. The process 800 is advantageous in thatit is more timely than human review and is less subjective than humanreview. Further, the process 800, in applying machine learning enginesto the image matrices, may develop a more accurate and more refinedinterpretation of the heart condition of the patient than had previouslybeen achieved through ECG measurement analysis and/or other sensoranalysis. The process 800 includes a number of engines. Each engine mayrepresent one or more software algorithms, computer-implementedfunctions, and/or hardware logic routines. Each engine may be configuredto be executed as processing commands executed on processing circuitryand/or routines implemented as a specialized circuit design, such as aprogrammable logic chip design.

In some implementations, one or more arrhythmia machine learning engines802 access the ECG image matrix 538 and/or the cardio-vibrational imagematrix 548 from the image matrix repository 540. For example, asdiscussed in relation to step 318 or step 320 of the method 300 of FIG.3A, the arrhythmia machine learning engine(s) 802 may apply the imagematrices 538, 548 to one or more machine learning classifiers to monitora cardiac condition. Advantageously, the arrhythmia machine learningengine(s) 802 may execute upon one or more graphics processing units(GPUs) to accelerate analysis of the ECG image matrix 538 and/or thecardio-vibrational image matrix 548. The arrhythmia machine learningengine(s) 802 may access a set of arrhythmia classifiers 804 forclassifying the contents of ECG image matrices and/or cardio-vibrationalimage matrices as corresponding to a type of arrhythmia.

The classifiers 804, in some embodiments, include one or more ECGclassifiers 804 trained using a truth base of ECG image matrices eachproduced using a same mapping of parameter values and pixelcharacteristic values as applied to producing the ECG image matrix 538.Additionally, ECG image matrices of the truth base used in training theECG classifier(s) 804 may have been produced using an ECG portionduration matching the ECG portion duration of the ECG image matrix 538.In a further example, the ECG image matrices of the truth base used intraining the ECG classifier(s) 804 may have been produced using a samedeflection feature registration to align the plotting of the ECG imagematrix 538.

In some embodiments, the classifiers 804 include one or morecardio-vibrational classifiers 804 trained using a truth base ofcardio-vibrational image matrices each produced using a same mapping ofparameter values and pixel characteristic values as applied to producingthe cardio-vibrational image matrix 548. Additionally,cardio-vibrational image matrices of the truth base used in training thecardio-vibrational classifier(s) 804 may have been produced using acardio-vibrational portion duration matching the cardio-vibrationalportion duration of the cardio-vibrational image matrix 548. In afurther example, the cardio-vibrational image matrices of the truth baseused in training the cardio-vibrational classifier(s) 804 may have beenproduced using a same deflection feature registration to align theplotting of the cardio-vibrational image matrix 548.

In some embodiments, the classifiers 804 include one or moreco-registered classifiers 804 trained using a truth base of ECG matricesco-registered with cardio-vibrational image matrices (e.g., alignedalong a y-axis or an x-axis as a single training image to demonstratecommonalities in cardiac signatures between the two types of matrices).As discussed above, each image matrix of the truth base, prior toco-registration, may have been produced using a same mapping ofparameter values and pixel characteristic values as applied to producingthe image matrix 538 or 548, a same portion duration as applied toproducing the image matrix 538 or 548, and or a same deflection featurefor registering alignment between the ECG image matrix 538 and thecardio-vibrational image matrix 548.

In some implementations, the arrhythmia classifiers 804 include separatearrhythmia classifiers trained from a truth base designed to identifyeach of supraventricular tachycardia (SVT), ventricular tachycardia,ventricular fibrillation, tachycardia, bradycardia, asystole, a heartpause condition, pulseless electrical activity, and/or atrialfibrillation. Thus, the arrhythmia classifiers 804, in an illustrativeexample, may include an ECG SVT classifier, an ECG ventriculartachycardia classifier, an ECG ventricular fibrillation classifier, anECG tachycardia classifier, an ECG bradycardia classifier, an ECGasystole classifier, an ECG heart pause condition classifier, an ECGpulseless electrical activity classifier, an ECG atrial fibrillationclassifier, a cardio-vibrational SVT classifier, a cardio-vibrationalventricular tachycardia classifier, a cardio-vibrational ventricularfibrillation classifier, a cardio-vibrational tachycardia classifier, acardio-vibrational bradycardia classifier, a cardio-vibrational asystoleclassifier, a cardio-vibrational heart pause condition classifier, acardio-vibrational pulseless electrical activity classifier, acardio-vibrational atrial fibrillation classifier, a co-registered SVTclassifier, a co-registered ventricular tachycardia classifier, aco-registered ventricular fibrillation classifier, a co-registeredtachycardia classifier, a co-registered bradycardia classifier, aco-registered asystole classifier, a co-registered heart pause conditionclassifier, a co-registered pulseless electrical activity classifier,and/or a co-registered atrial fibrillation classifier.

In some embodiments, the classifiers 804 include a normal heartcondition classifier. The normal heart condition classifier, forexample, may be trained using historic ECG image matrices and/orhistoric cardio-vibrational image matrices of the patient, therebyincluding the unique “fingerprint” of the cardiac cycles of the patient.Further, in certain embodiments, other of the classifiers 804 may betrained in part using historic image matrices derived from the patient.Patient-derived classifier(s) 804, for example, may be advantageous intraining the arrhythmia machine learning engine(s) 802 to recognizearrhythmia states in comparison to the unique features of the patient’scardiac cycles.

Additionally, or alternatively, in implementations, the machine learningengines(s) 802 can receive inputs apart from the image matrices for usein the classification (illustrated in FIG. 8 using dotted arrows).Examples of ECG metrics that can be used to aid in the classificationprocess include average heart rate, minimum heart rate, maximum heartrate, average RR interval in milliseconds or seconds, minimum RRinterval in milliseconds or seconds, maximum RR interval in millisecondsor seconds, standard deviation of RR intervals in milliseconds orseconds, number of successive RR intervals greater than a predeterminedduration (e.g., 45 ms) per minute, heart rate turbulence information(e.g., onset, slope, etc.), average QRS duration, standard deviation ofQRS duration, average QT interval, standard deviation of QT intervals,number of premature ventricular contractions (PVCs), and number ofconsecutive sequences of PVCs. Examples of cardio-vibrational metricsinclude S1, S2, S3, and S4 intensities, EMAT, %EMAT, LVST, %LVST, heartmurmur information, among others. Examples of demographic and clinicalmetrics 818 that can be used to aid in the classification processinclude age, patient gender, clinical status, e.g., explant of animplanted cardiac device, coronary artery disease patient or previouslyoperated on, prior myocardial infarction condition, prior VT/VFevent(s), among others. The demographic and clinical metrics 818, forexample, may be accessed from a patient information repository 816.

In some embodiments, at least a portion of the arrhythmia machinelearning engine(s) 802 include one or more deep neural network (DNN)models configured to apply at least a portion of the classifiers 804.DNN models, for example, perform regression and classification on datainput, potentially proving more successful where the classifiers 804 arecombined with additional metrics to classify the image matrices 538,548. In some embodiments, at least a portion of the arrhythmia machinelearning engine(s) 802 include one or more convolution neural network(CNN) models configured to apply at least a portion of the classifiers804. CNN models, for example, are designed to break down features of animage into sub-features, making CNN classification particularlyadvantageous for image classification. In some embodiments, at least aportion of the arrhythmia machine learning engine(s) 802 include one ormore network in network (NiN) models configured to apply at least aportion of the classifiers 804. NiN models, for example, take CNNprocessing to another level by analyzing a network of convolutionallayers of an image, proving advantageous for image classification. Otherdeep learning models may be applied, with the particular deep learningmodel being selected, in some examples, based in part on processingavailability and storage size availability in the end system (e.g., acloud network versus processing on a medical device), third party toolaccess (e.g., availability of cloud provider specialized tools andhardware for performing image classification), and/or processor type(e.g., GPU, CPU, FPGA, etc.).

In some embodiments, different image matrices are applied to identifydifferent types of arrhythmia. For example, heart pause and pulselesselectrical activity may be readily apparent using a single image matrix,such as the ECG image matrix 538, while other arrhythmias (e.g.,discerning between SVT and noise) may benefit from combined analysis ofboth the ECG image matrix 538 and the cardio-vibrational image matrix548.

The arrhythmia machine learning engine(s) 802, in some embodiments, areexecuted concurrently. For example, the arrhythmia machine learningengine(s) 802 may include a separate engine for each type of arrhythmia,with all types of arrhythmia scanned for in parallel by the arrhythmiamachine learning engine(s) 802 to determine the type of arrhythmia (orlack thereof, e.g., a determination of noise or of normal matrix plotpattern) as an arrhythmia classification 806. In some embodiments, thearrhythmia machine learning engine(s) 802 store the arrhythmiaclassification 806 to a repository of historic arrhythmiaclassifications 814. The arrhythmia classification 806, in someembodiments, includes a duration and/or a rate of arrhythmia. Theduration and/or rate, for example, may be relative (e.g., short, medium,long, slow, fast, etc.) or precise (e.g., number of seconds, number ofheartbeats per second, etc.). For example, the arrhythmia classifiers804 may include, for one or more types of arrhythmia, duration and/orrate refinements to more accurately classify the arrhythmia captured inthe ECG image matrix 538 and/or the cardio-vibrational image matrix 548.In one illustration, the heart rate associated with the bradycardia ortachycardia may be determined to aid in selecting a pacing routine as anaction 810. The arrhythmia machine learning engine(s) 802 may apply oneor more metrics in determining a refined arrhythmia classification 806.The additional metrics, in some examples, can include ECG metrics,cardio-vibrational metrics, and/or other physiological metrics (e.g.,breathing, blood pressure, body temperature, glucose level, tissuefluid, lung vibrations, etc.). The additional metrics, for example, maybe supplied by the medical device 502 (e.g., as described furtherbelow).

In some implementations, the arrhythmia machine learning engine(s) 802provide the arrhythmia classification 806 to a medical device actiondetermination engine 808. The medical device action determination engine808, responsive to receiving the arrhythmia classification 806,determines a selected action 810. The selected action 810 may involve,in some examples, doing nothing, collecting additional monitoring data(e.g., additional ECG image matrices and cardio-vibrational imagematrices) to monitor subsequent activity to determine whether acondition is ongoing, issuing a warning to a clinician, caretaker,and/or wearer of a cardiac monitoring device, or issuing a command to anelectrical therapeutic shock delivery engine 812 to deliver therapy tothe patient. In an illustrative example, as described in relation tosteps 322-332 of the method 300 of FIG. 3B, the arrhythmiaclassification may be used to determine whether to activatedefibrillation or to select a pacing routine.

In some implementations, the medical device action determination engine808 accesses ECG metrics 552 and/or cardio-vibrational metrics 554 fromthe metrics repository 556 to support the determination of the selectedaction 810. For example, the arrhythmia classification 806 may beanalyzed in light of heart rate metrics, heart rate variability metrics,PVC burden or counts, atrial fibrillation burden metrics, pauses, heartrate turbulence, QRS height, QRS width, cosine R-T, artificial pacing,QT interval, QT variability, T wave width, T wave alternans, T-wavevariability, ST segment changes, electromechanical activation time(EMAT), average EMAT, percentage of EMAT (% EMAT), systolic dysfunctionindex (SDI), and/or left ventricular systolic time (LVST). In someembodiments, further metrics are derived from other sensors of themedical device, such as a radio-frequency sensor, thermal sensor, one ormore medical grade microphones, and/or one or more accelerometers.

The further metrics, in some examples, may relate to blood oxygen level,body temperature, glucose levels, tissue fluid levels (e.g., thoracicfluid content), lung vibrations and/or breath vibrations, sleep relatedparameters (e.g., snoring, sleep apnea), blood pressure, arterial pulse,and/or heart wall movement. For example, a rhythm with a low bloodpressure could be treated differently than the same rhythm with a normalblood pressure. The same may be true for blood oxygen levels andthoracic fluid content. In a particular illustration, the rate of thearrhythmia may be determined from the heart rate metrics and/or heartrate variability metrics to support selection of an appropriatetreatment routine. In some embodiments, the medical device actiondetermination engine 808. The metrics 552 and 554, in some embodiments,include historic metrics derived from a timeframe prior to a timeframerepresented in the ECG image matrix 538 and/or the cardio-vibrationalimage matrix 548. For example, the metrics 552 and/or 554 may inform themedical device action determination engine 808 regarding a patient’sstate leading up to the arrhythmia classification 806. In someembodiments, the metrics 552 and 554 are representative of a same orsimilar timeframe as the timeframe represented in the ECG image matrix538 and/or the cardio-vibrational image matrix 548.

In some implementations, the medical device action determination engine808 accesses one or more historic arrhythmia classifications 814 from atimeframe prior to the timeframe represented in the ECG image matrix 538and/or the cardio-vibrational image matrix 548. For example, theselected action 810 on a prior round of execution of the process 800 mayhave been to continue to monitor, while, in determining that thecondition has persisted, the medical device action determination engine808 may determine, in the current round of execution, to command anelectrical shock therapeutic delivery engine 812 to deliver a selectedcourse of therapy.

In some implementations, the selected action 810 is provided to theelectrical therapeutic shock delivery engine 812 for providing therapyto the patient. As discussed in relation to step 328 of the method 300of FIG. 3B, the selected action 810 may be providing a defibrillatingshock. In another example, as discussed in relation to step 332 of themethod 300 of FIG. 3B, the selected action 810 may be a pacing routinecorresponding to the arrhythmia classification 806. The electricaltherapeutic shock delivery engine 812, for example, may be part oftherapy delivery circuitry 1002 of a medical device controller 1000 ofFIG. 10 or part of a processor 1018 commanding the therapy deliverycircuitry 1002, as illustrated in FIG. 10 .

In some implementations where electrical therapeutic shock is indicatedby the selected action 810, the process 800 is configured to causedelivery of the electrical therapeutic shock in between 30 seconds andaround two minutes after onset of the arrhythmia condition. In someexamples, electrical therapeutic shock may be delivered within a minuteto two minutes of arrhythmia onset, between thirty seconds and oneminute of arrhythmia onset, under thirty seconds of arrhythmia onset, orbetween around 5 seconds and around 30 seconds of arrhythmia onset.

FIG. 6 is a flow diagram of an example process 600 for presenting imagematrix data for review by a clinician. The process 600, in someexamples, may be performed at least in part by processing circuitry of amedical device such as a wearable medical device, by one or moreprocessors of a server or server system, or by one or more processors ofa cloud computing platform. Portions of the process 600, in someembodiments, are performed on different computing platforms. The process600 includes a number of engines. Each engine may represent one or moresoftware algorithms, computer-implemented functions, and/or hardwarelogic routines. Each engine may be configured to be executed asprocessing commands executed on processing circuitry and/or routinesimplemented as a specialized circuit design, such as a programmablelogic chip design.

In some implementations, one or more ECG signal graph generation engines602 access the ECG measurements 510 from the data repository 512 andgenerate, from the ECG measurements 510, an ECG graphic element 614. TheECG graphic element 614, for example, may be an ECG graphic element 704of the user interface 700 of FIG. 7A or an ECG graphic element 724 of auser interface 720 of FIG. 7B.

The ECG graphic element 614 may represent an electrocardiogram (e.g., agraph of voltage versus time) covering at least a portion of the timespan of the ECG image matrix 538. For example, the ECG graphic element704 of FIG. 7A includes a time scale along an x-axis 710 a, and avoltage magnitude along the y-axis 710 b. The time scale is 6 secondsalong the x-axis, as demonstrated by the x-axis 710 a (with readingsspanning from 6 mins to about 6.1 mins). The time scale of ECG graphicelement 704 tends to be shorter than the time scale for the ECG imagematrix (as noted, 6 seconds, when compared to 30 seconds for the ECGimage matrix 702), and as such an ECG reviewer will need to reviewmultiple additional ECG graphic elements 704. This is one of theadvantages of reviewing ECG data in the form of the ECG image matrixelements 702. For example, conventionally ECG technicians reviewbeat-by-beat ECG waveforms. Such review can take much time to review,for example, over 24 hours of ECG data. With the image matrixrepresentation, the ECG technician review is easier to identifyarrhythmia or abnormal beats with longer time scale than beat-to-beatECG representation. Rather than or in addition to the ECG measurements510, in some embodiments, the ECG signal graph generation engine(s) 602access the registered ECG measurements 526 of FIG. 5 (e.g., from thedata repository 512). As illustrated in FIG. 7A, for example, deflectionfeatures 714 a-i are arranged above the ECG signal plot in the graph704. Deflection features included in the ECG graphic element 614 maycorrespond to the deflection feature selection(s) 524 provided to thetime scale registration engine 522 of FIG. 5 . Similarly, turning toFIG. 7B, the ECG graphic element 724, plotted on an x-axis 730 a againsttime (e.g., 21.5 mins to 21.6 mins) and against voltage on a y-axis 730b, also includes a series of deflection points 734 a-i. The deflectionpoints 734 a-i, for example, may represent R peaks of the registered ECGmeasurements 526.

Returning to FIG. 6 , in some implementations, one or morecardio-vibrational signal graph generation engines 604 access thecardio-vibrational measurements 520 from the data repository 512 andgenerate, from the cardio-vibrational measurements 520, acardio-vibrational graphic element 616. The cardio-vibrational graphicelement 616, for example, may be a cardio-vibrational graphic element708 of the user interface 700 of FIG. 7A or a cardio-vibrational graphicelement 728 of a user interface 720 of FIG. 7B. The cardio-vibrationalgraphic element 616 may represent a cardio-vibrational signal (e.g., agraph of cardio-vibrations versus time) covering at least a portion ofthe time span of the cardio-vibrational image matrix 548. For example,the cardio-vibrational graphic element 708 of FIG. 7A includes a timescale along an x-axis 712 a, and a voltage magnitude along the y-axis712 b. The time scale for the cardio-vibrational graphic element 708 ofFIG. 7A is 6 seconds along the x-axis, as demonstrated by the x-axis 712a (with readings spanning from 21.5 mins to about 21.6 mins). Similar tothe ECG image graphic element described above, the time scale of thecardio-vibrational graphic element 708 tends to be shorter than the timescale for the cardio-vibrational image matrix graphic element 706 (asnoted, 6 seconds, when compared to 30 seconds for the cardio-vibrationalimage matrix graphic element 706), and as such a reviewer will need toreview multiple additional cardio-vibrational image matrix graphicelements 706. This is one of the advantages of reviewingcardio-vibrational data in the form of the cardio-vibrational imagematrix graphic elements 706. For example, conventionally ECG techniciansreview beat-by-beat ECG waveforms. Such review can take much time toreview, for example, over 24 hours of cardio-vibrational data. With theimage matrix representation, the technician review is easier to identifyarrhythmia or abnormal cardio-vibrational patterns with longer timescale than beat-to-beat cardio-vibrational representation. Rather thanor in addition to the cardio-vibrational measurements 520, in someembodiments, the cardio-vibrational signal graph generation engine(s)604 access the registered cardio-vibrational measurements 528 of FIG. 5(e.g., from the data repository 512). As illustrated in FIG. 7A, forexample, deflection features 716 a-i are arranged above thecardio-vibrational signal plot in the graph 708. Deflection featuresincluded in the cardio-vibrational graphic element 616 may correspond tothe deflection feature selection(s) 524 provided to the time scaleregistration engine 522 of FIG. 5 . Similarly, turning to FIG. 7B, thecardio-vibrational graphic element 728, plotted on an x-axis 712 aagainst time (e.g.,6 mins to 6.1 mins) and against voltage on a y-axis712 b, also includes a series of deflection points 736 a-i. Thedeflection points 734 a-i, for example, may represent R peaks of theregistered ECG measurements 526.

In some implementations, one or more image matrix graph generationengines 606 access the ECG image matrix 538 and the cardio-vibrationalimage matrix 548 from the image matrix repository 540 and produce one ormore image matrix graphic elements 618. In some embodiments, producingan image matrix graphic element 618 includes applying one or moreenhancements to the image matrix to improve visual interpretation by ahuman. As discussed in relation to step 116 of the method 100 of FIG. 1, for example, the image matrix graph generation engine(s) 606 may applya filtering or smoothing algorithm to the image matrix. The image matrixgraphic elements 618, for example may include, in addition to the imagematrix plots represented in the ECG image matrix 538 and thecardio-vibrational image matrix 548, time scale representations ormarkers and/or deflection feature representations or markers. Forexample, as illustrated in FIG. 7A, each of the ECG image matrix 702 andthe cardio-vibrational image matrix 706 includes an x-axis 718 a (30seconds) and a y-axis 718 b (1 second). Further, turning to FIG. 7B, thecardio-vibrational image matrix 726 includes a set of S-valueidentifiers 738a-738d marking approximate locations of the S1, S2, S3,and S4 values within the cardio-vibrational image matrix 726. The imagematrix graph generation engine(s) 606 may further access the mapping(s)of parameter values to pixel characteristic values 536. For example, theimage matrix graph generation engine(s) 606 may access or generate agraphic heat map key such as the heat map key 216 of thecardio-vibrational image matrix display 210 of FIG. 2B or the heat mapkey 404 of the ECG image matrix display 400 of FIG. 4 .

In some implementations, one or more metrics presentation engines 608access the ECG metrics 552 and/or the cardio-vibrational metrics 554from the metrics repository 556 and generate one or more metrics graphicelements 620. The metric graphic element(s) 620, in some examples, mayinclude heart rate (such as average, median, mode, or other statisticalmeasure of the heart rate, and/or maximum, minimum, resting,pre-exercise, and post-exercise heart rate values and/or ranges), heartrate variability metrics, PVC burden or counts, atrial fibrillationburden metrics, pauses, heart rate turbulence, QRS height, QRS width,changes in a size or shape of morphology of the ECG information, cosineR-T, artificial pacing, QT interval, QT variability, T wave width, Twave alternans, T-wave variability, and/or ST segment changes. Themetric graphic element(s) 620, in further examples, may includecardio-vibrational signal values including any one or all of S1, S2, S3,and S4, electromechanical activation time (EMAT), average EMAT,percentage of EMAT (% EMAT), systolic dysfunction index (SDI), and/orleft ventricular systolic time (LVST). The metric graphic element(s) 620may be represented as one or more graphs, tables, or markings upon oneor more of the ECG graphic element 614, the cardio-vibrational graphicelement 616, or the image matrix graphic element(s) 618.

In some implementations, a graphic user interface generation engine 610obtains the ECG graphic element 614, cardio-vibrational graphic element616, image matrix graphic element(s) 618, and/or metrics graphicelement(s) 620 and produces user interface display renderinginstructions 622 for rendering a graphic user interface (GUI) 624 upon adisplay 612 of a computing device for review by a user such as aclinician. The graphic interface 624, in some examples, may include theinterface 200 of FIG. 2A, the interface 210 of FIG. 2B, the interface400 of FIG. 4 , the interface 700 of FIG. 7A, or the interface 720 ofFIG. 7B. Turning to FIG. 7A, a set of illustrative arrows 719 a-719 e(depicted herein for illustrative purposes, and do not necessarily formpart of the interface 700) demonstrate correspondence to portions of theECG graph 704 and color bands of the ECG image matrix 702. The userinterface display rendering instructions 622, in some examples, caninclude one or more browser-renderable files such as hyper-text markuplanguage (HTML) files, Java Script (JS) files, Java Script Style Sheets(JSS) files, Cascading Style Sheets (CSS) files, and/or links to dataand/or graphics for presenting information on the display 612. The userinterface display rendering instructions 622, in some implementations,are updated in near real-time by the process 600 to continuouslygenerate display elements 614, 616, 618, and/or 620 while current imagematrices 538, 548 and measurements 510, 520 are received. The userinterface display rendering instructions 622, for example, may render amoving graphic visually plotting up-to-date information for the user’sreview. In further embodiments, the user interface display renderinginstructions 622 include download instructions for obtaining a reportfile, such as a portable document format (PDF) file or Visio spreadsheetincluding a combined report generated by the graphic user interfacegeneration engine 610. Rather than sending the user interface displayrendering instructions 622 to render the GUI 624 to the display 612, infurther embodiments, the report file may be shared electronically with auser through email, printing, facsimile, or other electroniccommunications.

In some implementations, a user reviewing the GUI 624 identifies one ormore anomalies in the images. For example, as illustrated in FIG. 7B, atiming anomaly 740 (e.g., an ectopic beat) shows up around 21.55 minutepoint of both the ECG image matrix 722 and the cardio-vibrational imagematrix 726. As further illustrated in FIG. 7B, the S3 and/or S4 bands738 c, 738 d are indicative of heart failure. Advantageously, while itwould be difficult to visually analyze the S3 bands 738 c or the S4bands 738 d from the cardio-vibrational graph 728, thecardio-vibrational image matrix 726 provides the end user a clearindication of problem regions (e.g., such as the fluctuating S3 738 cand/or S4 738 d).

FIGS. 9A and 9B are flow diagrams of example processes 900 and 920 forapplying machine learning classifiers to ECG image matrices and tocardio-vibrational image matrices to analyze heart risk in a patient.The processes 900 and 920 include a number of engines. Each engine mayrepresent one or more software algorithms, computer-implementedfunctions, and/or hardware logic routines. Each engine may be configuredto be executed as processing commands executed on processing circuitryand/or routines implemented as a specialized circuit design, such as aprogrammable logic chip design.

Turning to FIG. 9A, a process 900 is illustrated for analyzing potentialheart risk in a patient based upon a number of cardiac risk biomarkers.The process 900, for example, may provide advance warning of cardiacrisk based upon screening for the biomarkers through machine learninganalysis of a set of ECG image matrices 538 a-n and/or a set ofcardio-vibrational image matrices 548 a-n. The process 900, for example,may be used to monitor patient health, determine a best course oftreatment, and/or manage appropriate follow up care for a patient basedupon a prediction of risk of future cardiac disease or disorder. Theprocess 900 advantageously uses a set of cardiac risk biomarkerclassifiers 904 developed through machine learning techniques, alongwith other health metrics, to provide a predictive assessment of thepatient’s future cardiac health. Further, as more information is learnedregarding correspondence between cardiac patterns and future cardiacoutcomes through ongoing training of the cardiac risk biomarkerclassifiers 904, the process 900 becomes more and more refined in itsheart risk predictive analysis.

In some implementations, the process 900 begins with one or more heartrisk machine learning engines 902 obtaining the set of ECG imagematrices 538 a-n and the set of cardio-vibrational image matrices 548a-n from the image matrix repository 540. The sets of image matrices 538a-n and 548 a-n, in some embodiments, represent a substantiallytime-contiguous set of image matrices representing, together, a numberof adjacent periods of time over a monitoring period. The adjacentperiods of time, for example, may be directly adjacent, partiallyoverlapping, or separated by a time gap (e.g., representing a time untila next deflection feature for aligning the next cardio-vibrational imagematrix 548 x with the next ECG image matrix 548 x). In some embodiments,the sets of image matrices 538 a-n and 548 a-n represent a series ofdiscrete periods of time captured throughout the monitoring period. Thediscrete periods of time, in some examples, may extend between around 15seconds and around 30 seconds, between around 30 seconds and around 45seconds, between around 45 seconds and around 60 seconds, between around60 seconds and around 90 seconds, between around 90 seconds and 120seconds, between around 2 minutes or around 3 minutes, or between around3 minutes and around 10 minutes.

The sampling intervals throughout the monitoring period for capturingthe discrete periods of time, in some examples, can include a periodicsampling, an activity-based sampling capturing one or more patientactivities (e.g., sampling during high activity, sampling during sleep,etc.), and/or a patient-triggered sampling (e.g., patient activates acontrol on a user interface on or in communication with a wearablecardiac monitoring device to trigger sampling whenever experiencing oneor more symptoms). The monitoring period may extend from about threehours to about one month. The monitoring period, in some examples, maycover at least one hour, around one hour to three hours, around threehours to twelve hours, around twelve hours to one day, around one day tothree days, around three days to one week, around one week to one month,or around one month to three months.

In some implementations, the heart risk machine learning engine(s) 902determine at least one heart risk classification 906 through applying aset of cardiac risk biomarker classifiers 904 to the sets of imagematrices 538 a-n and 548 a-n. Advantageously, the heart risk machinelearning engine(s) 902 may execute upon one or more graphics processingunits (GPUs) to accelerate analysis of the sets of image matrices 538a-n and 548 a-n. The set of cardiac risk biomarker classifiers 904, insome examples, may be trained to detect cardiac risk biomarkersassociated with sudden cardiac arrest (SCA), low ejection fraction (EF),or a stage of heart failure. The cardiac risk biomarkers, in someexamples, may include EMAT (e.g., over 120 milliseconds), LVST (e.g.,under 0.001), S3 intensity (e.g., at or above 5), S3 width, pre-ejectionperiod (PEP), and/or ejection time (ET). In some examples, the cardiacrisk biomarkers include thoracic fluid index and a percentage of lungfluid over a period of time.

The cardiac risk biomarker classifiers 904 associated with SCA, in someembodiments, are trained to detect ventricular fibrillation (VF),ventricular tachycardia (VT), pulseless electrical activity (PEA),and/or asystole. The cardiac risk biomarker classifiers 904 associatedwith low EF, in some embodiments, are trained to detect a less than 35%classification, a 35 to 39% classification, and a 40% to 54%classification. The heart failure stages, in some embodiments, alignwith the New York Heart Association (NYHA) heart failureclassifications. The NYHA classifications are as follows:

-   Class I - No symptoms and no limitation in ordinary physical    activity, e.g. shortness of breath when walking, climbing stairs    etc.-   Class II - Mild symptoms (mild shortness of breath and/or angina)    and slight limitation during ordinary activity.-   Class III - Marked limitation in activity due to symptoms, even    during less-than-ordinary activity, e.g. walking short distances    (20-100 m). Comfortable only at rest.-   Class IV - Severe limitations. Experiences symptoms even while at    rest. Mostly bedbound patients.

The cardiac risk biomarker classifiers 904, in some embodiments, includeone or more ECG classifiers 904 trained using a truth base of ECG imagematrices each produced using a same mapping of parameter values andpixel characteristic values as applied to producing the ECG imagematrices 538 a-n. Additionally, ECG image matrices of the truth baseused in training the ECG classifier(s) 904 may have been produced usingan ECG portion duration matching the ECG portion duration of the ECGimage matrices 538 a-n. In a further example, the ECG image matrices ofthe truth base used in training the ECG classifier(s) 904 may have beenproduced using a same deflection feature registration to align theplotting of the ECG image matrices 538 a-n.

In some embodiments, the cardiac risk biomarker classifiers 904 includeone or more cardio-vibrational classifiers 904 trained using a truthbase of cardio-vibrational image matrices each produced using a samemapping of parameter values and pixel characteristic values as appliedto producing the cardio-vibrational image matrices 548 a-n.Additionally, cardio-vibrational image matrices of the truth base usedin training the cardio-vibrational classifier(s) 904 may have beenproduced using a cardio-vibrational portion duration matching thecardio-vibrational portion duration of the cardio-vibrational imagematrices 548 a-n. In a further example, the cardio-vibrational imagematrices of the truth base used in training the cardio-vibrationalclassifier(s) 904 may have been produced using a same deflection featureregistration to align the plotting of the cardio-vibrational imagematrices 548 a-n.

In some embodiments, the cardiac risk biomarker classifiers 904 includeone or more co-registered classifiers 904 trained using a truth base ofECG matrices co-registered with cardio-vibrational image matrices (e.g.,aligned along a y-axis or an x-axis as a single training image todemonstrate commonalities in cardiac signatures between the two types ofmatrices). As discussed above, each image matrix of the truth base,prior to co-registration, may have been produced using a same mapping ofparameter values and pixel characteristic values as applied to producingthe image matrices 538 a-n or 548 a-n, a same portion duration asapplied to producing the image matrices 538 a-n or 548 a-n, and or asame deflection feature for registering alignment between the ECG imagematrices 538 a-n and the cardio-vibrational image matrices 548 a-n.

In some implementations, the cardiac risk biomarker classifiers 904include separate cardiac risk biomarker classifiers trained from a truthbase designed to identify each of VF, VT, PEA, asystole, less than 35%EF, 35 to 39% EF, 40% to 54% EF, class I HF, class II HF, class III HF,and class IV HF. Thus, the cardiac risk biomarker classifiers 904, in anillustrative example, may include an ECG VF classifier, an ECG VTclassifier, an ECG PEA classifier, an ECG asystole classifier, an ECGless than 35% EF classifier, an ECG 35 to 39% EF classifier, an ECG 40%to 54% EF classifier, an ECG class I HF classifier, an ECG class II HFclassifier, an ECG class III HF classifier, an ECG class IV HFclassifier, a cardio-vibrational VF classifier, a cardio-vibrational VTclassifier, a cardio-vibrational PEA classifier, a cardio-vibrationalasystole classifier, a cardio-vibrational less than 35% EF classifier, acardio-vibrational 35 to 39% EF classifier, a cardio-vibrational 40% to54% EF classifier, a cardio-vibrational class I HF classifier, acardio-vibrational class II HF classifier, a cardio-vibrational classIII HF classifier, a cardio-vibrational class IV HF classifier, aco-registered VF classifier, a co-registered VT classifier, aco-registered PEA classifier, a co-registered asystole classifier, aco-registered less than 35% EF classifier, a co-registered 35 to 39% EFclassifier, a co-registered 40% to 54% EF classifier, a co-registeredclass I HF classifier, a co-registered class II HF classifier, aco-registered class III HF classifier, and/or a co-registered class IVHF classifier.

In some embodiments, the cardiac risk biomarker classifiers 904 includea normal heart condition classifier. The normal heart conditionclassifier, for example, may be trained using historic ECG imagematrices and/or historic cardio-vibrational image matrices of thepatient, thereby including the unique “fingerprint” of the cardiaccycles of the patient. Further, in certain embodiments, other of thecardiac risk biomarker classifiers 904 may be trained in part usinghistoric image matrices derived from the patient. Patient-derivedcardiac risk biomarker classifier(s) 904, for example, may beadvantageous in training the heart risk machine learning engine(s) 902to recognize cardiac anomalies in comparison to the unique features ofthe patient’s cardiac cycles.

In some embodiments, at least a portion of the heart risk machinelearning engine(s) 902 include one or more deep neural network (DNN)models configured to apply at least a portion of the cardiac riskbiomarker classifiers 904. In some embodiments, at least a portion ofthe heart risk machine learning engine(s) 902 include one or moreconvolution neural network (CCN) models configured to apply at least aportion of the cardiac risk biomarker classifiers 904. In someembodiments, at least a portion of the heart risk machine learningengine(s) 902 include one or more network in network (NiN) modelsconfigured to apply at least a portion of the cardiac risk biomarkerclassifiers 904. Other deep learning models may be applied, with theparticular deep learning model being selected, in some examples, basedin part on processing availability and storage size availability in theend system (e.g., a cloud network versus processing on a medicaldevice), third party tool access (e.g., availability of cloud providerspecialized tools and hardware for performing image classification),and/or processor type (e.g., GPU, CPU, FPGA, etc.).

In some embodiments, different image matrices are applied to identifydifferent types of cardiac risk biomarkers. In examples, thecardio-vibrational signals are all those that are solely or include inthe calculation any cardiac data from a vibrational sensor, includingS1, S2, S3, S4, EMAT, SDI, among others.

The heart risk learning engine(s) 902, in some embodiments, are executedconcurrently. For example, the heart risk learning engine(s) 902 mayinclude a separate engine for each of at least SCA, HF, and EF, with theseparate engines executing in parallel to determine evidence of any orall of SCA, HF, and EF. The positive identifiers (e.g., classifiermatches), in some implementations, are combined as a heart riskclassification 906. The heart risk classification 906, for example, mayinclude a feature vector or binary code indicating a corresponding“match” or “no match” related to each of the cardiac risk biomarkerclassifiers 904. In another example, the heart risk classification 906may include a nuanced analysis, including information regardingconfidence levels associated with various matching cardiac riskbiomarker classifiers 904. The heart risk learning engine(s) 902, insome implementations, apply weightings related to one or more of thecardiac risk biomarker classifiers 904 in determining the heart riskclassification 906. For example, in relation to SCA, VF and VT may beweighted less than PEA and asystole, since treatability and likelihoodof survival are more promising with VF and VT. The weightings may bebased, in part, upon a confidence the heart risk machine learningengine(s) 902 have in the identified match for each of one or more ofthe cardiac risk biomarker classifiers 904. For example, in machinelearning analysis, matches of a particular input to a classifier areassociated with a score, or confidence rating, in how close the matchappears. At some point, the confidence rating may be so low that theheart risk learning engine(s) 902 consider the outcome to be a non-match(e.g., 65% certainty, between 70% and 85% certainty, below 95%certainty, etc.). In other ranges, the confidence rating may be at athreshold for inclusion but weighted lower than a higher confidencerating. In an illustrative example, below 70% certainty may beconsidered a non-match, between 70% and 85% a possible match, between85% and 95% a probable match, and over 95% a confident match. Each ofpossible, probable, and confident may be associated with a differentweighting for formulating the heart risk classification 906.

In some implementations, the heart risk classification 906 is providedto a heart risk analysis engine 908 for analysis in view of patientdemographic information 910 as well as ECG metrics 552 and/orcardio-vibrational metrics 554 accessed from the metrics repository 556.The patient demographic information 910, in some examples, may includegender, age, height, weight, and/or BMI. Further, the patientdemographic information 910 may include diseases or disorders such as,in some examples, diabetes, anemia, renal failure, sleep apnea, and/orcognitive disfunction. The heart risk analysis engine 908, in someembodiments, further considers additional patient physiological metrics916. The patient physiological metrics 916, in some examples, mayinclude breathing, blood pressure, body temperature, glucose level,tissue fluid, and/or lung vibrations. The additional metrics, forexample, may be supplied by the medical device 502 of FIG. 5 (e.g., asdescribed further in the Example Medical Devices for Monitoring aPatient’s Heart Condition section, below).

The heart risk analysis engine 908, in some embodiments, generates heartrisk metrics 912. The metrics, in some implementations, include valuescalculated from the ECG image matrices 538 a-n and/or thecardio-vibrational image matrices 548 a-n. For example, upon indication,in the heart risk classification 906, that the EMAT, LVST, S3 intensity,and/or S3 width is potentially indicative of heart risk, mathematicalcalculations may be generated by the heart risk analysis engine 908using the image matrices 538 a-n and/or 548 a-n to apply a valuation tothe abnormality. In some implementations, the metrics include valuesobtained from the ECG metrics 552 and/or cardio-vibrational metrics 554.The heart risk metrics 912, further, may include calculations performedusing mathematical algorithms to quantify heart risk based upon theheart risk classification 906, patient physiological metrics 916, and/orpatient demographic information 910. In an illustrative example, basedon a heart risk classification 906 indicating significant risk of SCA,ECG metrics 552 and/or cardio-vibrational metrics 554 may be provided toan algorithm, as well as patient demographics 910 and/or patientphysiological metrics 916, to quantify the risk in light of other knowninformation regarding the patient. The metrics, in some implementations,include metrics pertaining to each of the areas screened for using thecardiac risk biomarker classifiers 904 (e.g., SCA, EF, and HF).

In some implementations, a heart risk report generation engine 914combines the heart risk metrics 912 in a report 918 for review by aclinician and/or patient. The report 918, for example, may includemetrics 912 such as heart rate (such as average, median, mode, or otherstatistical measure of the heart rate, and/or maximum, minimum, resting,pre-exercise, and post-exercise heart rate values and/or ranges), heartrate variability metrics, PVC burden or counts, atrial fibrillationburden metrics, pauses, heart rate turbulence, QRS height, QRS width,changes in a size or a shape of morphology of the ECG information,cosine R-T, artificial pacing, QT interval, QT variability, T wavewidth, T wave alternans, T-wave variability, and/or ST segment changes.The metrics 912 presented in the report 918, in further examples, mayinclude cardio-vibrational signal values including any one or all of S1,S2, S3, and S4, electromechanical activation time (EMAT), average EMAT,percentage of EMAT (% EMAT), systolic dysfunction index (SDI), and/orleft ventricular systolic time (LVST). Certain metrics 912 may berepresented in the heart risk report 918 as one or more graphs and/ortables.

In some implementations, the heart risk report generation engine 914produces user interface display rendering instructions for rendering theheart risk report 918 as a graphic user interface (GUI) upon a displayof a computing device for review by a user such as a clinician. In someimplementations, the heart risk report 918 is generated by the heartrisk report generation engine 914 as a report file, such as a portabledocument format (PDF) file or Visio spreadsheet. The heart risk reportgeneration engine 914 may provide the report to a computer display, to aprinter, or to an electronic communications contact (e.g., emailaddress), in some examples.

Turning to FIG. 9B, a process 920 is illustrated for analyzing heartfailure trends in a patient based upon a number of heart failurebiomarkers. The process 920, for example, may provide an objectiveassessment of heart failure progression in the patient based uponscreening for the biomarkers through machine learning analysis of a setof historic ECG image matrices 538 a-n and/or a set of historiccardio-vibrational image matrices 548 a-n. The process 920, for example,may be used to monitor patient health, determine whether a course oftreatment appears to be successful in mitigating worsening of heartfailure, and/or manage appropriate follow up care for a patient basedupon a present assessment of heart failure trends in the patient. Theprocess 920 advantageously uses a set of heart failure biomarkerclassifiers 924 developed through machine learning techniques, alongwith other health metrics, to provide an objective analysis of heartfailure trends in the patient. Further, as more information is learnedregarding correspondence between cardiac patterns and degeneration ofheart condition through the classes or stages of heart failure throughongoing training of the heart failure biomarker classifiers 924, theprocess 920 becomes more and more refined in its heart failure trendanalysis.

In some implementations, the process 920 begins with one or more heartfailure progression machine learning engines 922 obtaining a set ofhistoric ECG image matrices 936 a-n and a set of historiccardio-vibrational image matrices 938 a-n from a historic image matrixrepository 940. The sets of historic image matrices 936 a-n and 938 a-n,for example, may include the image matrices 538 a-n and/or 548 a-n usedto perform heart risk analysis in accordance to the process 900 of FIG.9A. The sets of image matrices 936 a-n and 938 a-n, in some embodiments,include one or more image matrices 936 a-n and 938 a-n captured on eachcapture period of a series of capture periods. Image matrices from agiven capture period, as described in relation to FIG. 9A, may representa substantially time-contiguous set of image matrices, or a series ofdiscrete periods of time captured throughout the given capture period.The capture periods, in some examples, may cover at least one hour,around one hour to three hours, around three hours to twelve hours,around twelve hours to one day, around one day to three days, or aroundthree days to one week. In some implementations, the capture periods areperiodic. For example, the capture periods may be every week, everyother week, every month, every other month, every three months, or everysix months. The capture periods may correspond to check-up visits with asurgeon, doctor, or other clinician. The series of capture periods, insome embodiments, grow as the patient continues to be monitored. Forexample, upon a first execution of the process 920 for the patient, theseries of capture periods may represent two capture periods. For thenext analysis, the patient’s image matrices 936 a-n and 938 a-n mayrepresent three capture periods, and so on.

In some implementations, the heart failure progression machine learningengine(s) 922 determine at least one heart failure classification 926through applying a set of heart failure classifiers 924 to the sets ofhistoric image matrices 936 a-n and 938 a-n.

Advantageously, the heart failure progression machine learning engine(s)922 may execute upon one or more graphics processing units (GPUs) toaccelerate analysis of the sets of historic image matrices 936 a-n and938 a-n. The set of heart failure classifiers 924, in some examples, maybe trained to detect heart failure biomarkers associated with a stage ofheart failure. The heart failure stages, in some embodiments, align withthe New York Heart Association (NYHA) heart failure classifications. TheNYHA classifications are as follows:

-   Class I - No symptoms and no limitation in ordinary physical    activity, e.g. shortness of breath when walking, climbing stairs    etc.-   Class II - Mild symptoms (mild shortness of breath and/or angina)    and slight limitation during ordinary activity.-   Class III - Marked limitation in activity due to symptoms, even    during less-than-ordinary activity, e.g. walking short distances    (20-100 m). Comfortable only at rest.-   Class IV - Severe limitations. Experiences symptoms even while at    rest. Mostly bedbound patients.

For example, the heart failure classifiers 924 may include a portion ofthe heart risk classifiers 904.

In some implementations, the set of heart failure biomarker classifiers924 are trained based on a pre-existing corpus of image matricesdemonstrating each of a set of progressions and/or stages of heartfailure, such as the four classes identified above. The set of heartfailure classifiers 924, in some embodiments, include one or more ECGclassifiers 924 trained using a truth base of ECG image matrices eachproduced using a same mapping of parameter values and pixelcharacteristic values as applied to producing the historic ECG imagematrices 936 a-n. Additionally, ECG image matrices of the truth baseused in training the ECG classifier(s) 924 may have been produced usingan ECG portion duration matching the ECG portion duration of thehistoric ECG image matrices 936 a-n. In a further example, the ECG imagematrices of the truth base used in training the ECG classifier(s) 924may have been produced using a same deflection feature registration toalign the plotting of the historic ECG image matrices 936 a-n.

In some embodiments, the heart failure biomarker classifiers 924 includeone or more cardio-vibrational classifiers 924 trained using a truthbase of cardio-vibrational image matrices each produced using a samemapping of parameter values and pixel characteristic values as appliedto producing the historic cardio-vibrational image matrices 938 a-n.Additionally, cardio-vibrational image matrices of the truth base usedin training the cardio-vibrational classifier(s) 924 may have beenproduced using a cardio-vibrational portion duration matching thecardio-vibrational portion duration of the historic cardio-vibrationalimage matrices 938 a-n. In a further example, the cardio-vibrationalimage matrices of the truth base used in training the cardio-vibrationalclassifier(s) 924 may have been produced using a same deflection featureregistration to align the plotting of the historic cardio-vibrationalimage matrices 938 a-n.

In some embodiments, the heart failure biomarker classifiers 924 includeone or more co-registered classifiers 924 trained using a truth base ofECG matrices co-registered with cardio-vibrational image matrices (e.g.,aligned along a y-axis or an x-axis as a single training image todemonstrate commonalities in cardiac signatures between the two types ofmatrices). As discussed above, each image matrix of the truth base,prior to co-registration, may have been produced using a same mapping ofparameter values and pixel characteristic values as applied to producingthe historic image matrices 936 a-n or 938 a-n, a same portion durationas applied to producing the historic image matrices 936 a-n or 938 a-n,and or a same deflection feature for registering alignment between thehistoric ECG image matrices 936 a-n and the historic cardio-vibrationalimage matrices 938 a-n.

In some implementations, the heart failure biomarker classifiers 924include separate cardiac risk biomarker classifiers trained from a truthbase designed to identify each of class I HF, class II HF, class III HF,and class IV HF. Thus, the heart failure biomarker classifiers 924, inan illustrative example, may include an ECG class I HF classifier, anECG class II HF classifier, an ECG class III HF classifier, an ECG classIV HF classifier, a cardio-vibrational class I HF classifier, acardio-vibrational class II HF classifier, a cardio-vibrational classIII HF classifier, a cardio-vibrational class IV HF classifier, aco-registered class I HF classifier, a co-registered class II HFclassifier, a co-registered class III HF classifier, and/or aco-registered class IV HF classifier.

Each of the heart failure biomarker classifiers 924, in someembodiments, were trained in part using a portion of the historic ECGimage matrices 936 a-n and/or the historic cardio-vibrational imagematrices 938 a-n. For example, the heart failure biomarker classifiers924 may be trained in part to identify a baseline (e.g., priorclassification of heart failure) signature of the patient to betteranalyze trends in worsening of the heart failure condition.

In some embodiments, at least a portion of the heart failure progressionmachine learning engine(s) 922 include one or more deep neural network(DNN) models configured to apply at least a portion of the heart failurebiomarker classifiers 924. In some embodiments, at least a portion ofthe heart failure progression machine learning engine(s) 922 include oneor more convolution neural network (CCN) models configured to apply atleast a portion of the heart failure biomarker classifiers 924. In someembodiments, at least a portion of the heart failure progression machinelearning engine(s) 922 include one or more network in network (NiN)models configured to apply at least a portion of the heart failurebiomarker classifiers 924. Other deep learning models may be applied,with the particular deep learning model being selected, in someexamples, based in part on processing availability and storage sizeavailability in the end system (e.g., a cloud network versus processingon a medical device), third party tool access (e.g., availability ofcloud provider specialized tools and hardware for performing imageclassification), and/or processor type (e.g., GPU, CPU, FPGA, etc.).

The heart failure progression machine learning engine(s) 922, in someembodiments, are executed concurrently. For example, the heart risklearning engine(s) 902 may include a separate engine for each of atleast class I, class II, class III, and class IV identification, withthe separate engines executing in parallel to determine biomarkersindicative of each class or stage of heart failure. The positiveidentifiers (e.g., classifier matches), in some implementations, arecombined as a heart failure classification 926. The heart failureclassification 926, in some embodiments, is stored to a historic heartfailure classifications repository 928 for future review. The heartfailure classification 926, for example, may include a feature vector orbinary code indicating a corresponding “match” or “no match” related toeach of the heart failure biomarker classifiers 924. In another example,the heart failure classification 926 may include a nuanced analysis,including information regarding confidence levels associated withvarious matching heart failure biomarker classifiers 924. The heartfailure progression learning engine(s) 922, in some implementations,apply weightings related to one or more of the heart failure biomarkerclassifiers 924 in determining the heart failure classification 926. Theweightings may be based, in part, upon a confidence the heart failureprogression machine learning engine(s) 922 have in the identified matchfor each of one or more of the heart failure biomarker classifiers 924.For example, in machine learning analysis, matches of a particular inputto a classifier are associated with a score, or confidence rating, inhow close the match appears. At some point, the confidence rating may beso low that the heart failure progression learning engine(s) 922consider the outcome to be a non-match (e.g., 65% certainty, between 70%and 85% certainty, below 95% certainty, etc.). In other ranges, theconfidence rating may be at a threshold for inclusion but weighted lowerthan a higher confidence rating. In an illustrative example, below 70%certainty may be considered a non-match, between 70% and 85% a possiblematch, between 85% and 95% a probable match, and over 95% a confidentmatch. Each of possible, probable, and confident may be associated witha different weighting for formulating the heart failure classification926.

In some implementations, the heart failure classification 926 isprovided to a heart failure trend analysis engine 930 for analysis ofthe heart failure classification 926 in view of historic heart failureclassification(s) 928 for the patient (e.g., obtained during a prioranalysis, such as through the process 900 of FIG. 9A). Thisadvantageously allows the process 920 to automatically analyze thepatient for changes in classification indicative of heart failureworsening, leading to early identification of worsening heart failurecondition and thus early intervention. The results of the comparison bythe heart failure trend analysis engine 930 include heart failure trendmetrics 932, for example identifying no change in class or stage ofheart failure, a negative trend in heart failure progression, or apositive trend in heart failure progression. In some examples, apositive trend in heart failure progression can include decompensationand recovery of cardiac function in the heart failure patient.

The heart failure trend analysis engine 930 may combine comparisonanalysis of the heart failure classification 926 and the historic heartfailure classification(s) 928 with analysis of historic ECG metrics 552a-n and historic cardio-vibrational metrics 554 a-n, accessed from themetrics repository 556, to determine heart failure trend metrics 932 forthe patient. The historic ECG metrics 552 a-n and historiccardio-vibrational metrics 554 a-n, for example, may include at least amost recent set of metrics 552, 554 as well as a prior set of metrics552, 554 (e.g., from prior execution of the process 920 of FIG. 9B orthe process 900 of FIG. 9A). The historic metrics 552 a-n and/or 554a-n, in some examples, may include heart rate (such as average, median,mode, or other statistical measure of the heart rate, and/or maximum,minimum, resting, pre-exercise, and post-exercise heart rate valuesand/or ranges), heart rate variability metrics, PVC burden or counts,atrial fibrillation burden metrics, pauses, heart rate turbulence, QRSheight, QRS width, changes in a size or a shape of morphology of the ECGinformation, cosine R-T, artificial pacing, QT interval, QT variability,T wave width, T wave alternans, T-wave variability, ST segment changes,S1, S2, S3, S4, electromechanical activation time (EMAT), average EMAT,percentage of EMAT (% EMAT), systolic dysfunction index (SDI), and/orleft ventricular systolic time (LVST). The heart failure trend metrics932, for example, may include differences or significant changes in oneor more of the above identified metrics.

In some implementations, a heart failure trend report generation engine934 combines the heart failure trend metrics 932 in a report for reviewby a clinician and/or patient.

The report 936, for example, may include one or more graphs and/ortables representing progressive changes in the historic metrics 552 a-nand/or 554 a-n as well as a present heart failure classificationassessment.

In some implementations, the heart failure trend report generationengine 934 produces user interface display rendering instructions forrendering the heart failure trend report 936 as a graphic user interface(GUI) upon a display of a computing device for review by a user such asa clinician. In some implementations, the failure trend report 936 isgenerated by the heart failure trend report generation engine 934 as areport file, such as a portable document format (PDF) file or Visiospreadsheet. The heart failure trend report generation engine 934 mayprovide the report to a computer display, to a printer, or to anelectronic communications contact (e.g., email address), in someexamples.

In training the machine learning classifiers as described herein, acase-control cohort may be built of a large number of patients (e.g.,between 5,000-10,000, or more) with demographic data including gender,age, and ICD code information. For example, these patients may bepatients that have worn the LifeVest® wearable cardioverterdefibrillator (WCD) device over a certain span of years (e.g., patientsfrom between 2014 and 2020) and provided ambulatory ECG andcardio-vibrational recordings. In implementations, this training datasetcan be enriched with information about which of these patients wereshocked for sustained VT/VF (e.g., tachyarrhythmia SCA) within a certaintime from initial wear (e.g., 72 hours, 1 week, 10 days, or 2 weeks).Controls can include patients who did not receive shocks but werematched to cases based on age, gender and initial wear time. Duringtraining, the machine learning classifiers can be based on a scorecharacterizing likelihood of sudden cardiac arrest or otherpredetermined end point as described above.

A second training set can be built using a second cohort of a largenumber of patients (e.g., 15,000-20,000, or more) in a similar manner asdescribed above for the first training set. Training Set 2 can beaccrued to establish threshold for stratifying patients by their riskfor adverse end points. In this example, the second training set can beused to establish threshold values from the models that optimizessensitivity and specificity for detecting increased risk of the adverseend points.

In the foregoing manner, an ROC curve can be generated indicatingacceptable thresholds for sensitivity and specificity, in accordancewith a user’s specifications. For example, a 92% specificity level maybe chosen to reduce the potential for alarm fatigue and increaseconfidence in the risk status. In examples, the specificity that is setthen determines the sensitivity.

In example advantages or benefits of the disclosure herein, as a firstexample, a risk status may be applied to ensuring patients alreadyprescribed a medical device remain protected during periods of increasedrisk. Some patients may choose to end their medical device use withinthe first few days of wear (e.g., after only a week or two weeks). Thismay be a result of a lack of clear understanding of the risk they faceand the related role of the wearable medical devices. Being able toidentify patients who are at the highest level of risk who wish to enduse prematurely may provide the caregiver an opportunity to engage thepatient and ensure that they understand the importance of sudden cardiacrisk protection. As a second advantage, a risk status may be applied topatients perceived as having lower sudden cardiac arrest risk, includingpatients with preserved ejection fraction, who are under ambulatorycardiac monitoring (e.g., Holter-type monitoring). Such patients may nototherwise be identified as such and can be switched over to a wearablecardioverter defibrillator for protection against sudden cardiac arrest.

The teachings of the present disclosure can be generally applied toexternal medical monitoring and/or treatment devices that include one ormore sensors as described herein. Such external medical devices caninclude, for example, ambulatory medical devices as described hereinthat are capable of and designed for moving with the patient as thepatient goes about his or her daily routine. An example ambulatorymedical device can be a wearable medical device such as a wearablecardioverter defibrillator (WCD), an in-hospital device such as anin-hospital wearable defibrillator (HWD), a short-term wearable cardiacmonitoring and/or therapeutic device, mobile cardiac event monitoringdevices, and other similar wearable medical devices.

The wearable medical device can be capable of continuous use by thepatient. In some implementations, the continuous use can besubstantially or nearly continuous in nature. That is, the wearablemedical device can be continuously used, except for sporadic periodsduring which the use temporarily ceases (e.g., while the patient bathes,while the patient is refit with a new and/or a different garment, whilethe battery is charged/changed, while the garment is laundered, etc.).Such substantially or nearly continuous use as described herein maynonetheless be considered continuous use. For example, the wearablemedical device can be configured to be worn by a patient for as many astwenty-four hours a day. In some implementations, the patient can removethe wearable medical device for a short portion of the day (e.g., forhalf an hour to bathe).

Further, the wearable medical device can be configured as a long term orextended use medical device. Such devices can be configured to be usedby the patient for an extended period of several days, weeks, months, oreven years. In some examples, the wearable medical device can be used bya patient for an extended period of at least one week. In some examples,the wearable medical device can be used by a patient for an extendedperiod of at least 30 days. In some examples, the wearable medicaldevice can be used by a patient for an extended period of at least onemonth. In some examples, the wearable medical device can be used by apatient for an extended period of at least two months. In some examples,the wearable medical device can be used by a patient for an extendedperiod of at least three months. In some examples, the wearable medicaldevice can be used by a patient for an extended period of at least sixmonths. In some examples, the wearable medical device can be used by apatient for an extended period of at least one year. In someimplementations, the extended use can be uninterrupted until a physicianor other HCP provides specific instruction to the patient to stop use ofthe wearable medical device.

Regardless of the extended period of wear, the use of the wearablemedical device can include continuous or nearly continuous wear by thepatient as described above. For example, the continuous use can includecontinuous wear or attachment of the wearable medical device to thepatient, e.g., through one or more of the electrodes as describedherein, during both periods of monitoring and periods when the devicemay not be monitoring the patient but is otherwise still worn by orotherwise attached to the patient. The wearable medical device can beconfigured to continuously monitor the patient for cardiac-relatedinformation (e.g., ECG information, including arrhythmia information,cardio-vibrations, etc.) and/or non-cardiac information (e.g., bloodoxygen, the patient’s temperature, glucose levels, tissue fluid levels,and/or lung vibrations). The wearable medical device can carry out itsmonitoring in periodic or aperiodic time intervals or times. Forexample, the monitoring during intervals or times can be triggered by auser action or another event.

As noted above, the wearable medical device can be configured to monitorother non-ECG physiologic parameters of the patient in addition tocardiac related parameters. For example, the wearable medical device canbe configured to monitor, for example, pulmonary-vibrations (e.g., usingmicrophones and/or accelerometers), breath vibrations, sleep relatedparameters (e.g., snoring, sleep apnea), tissue fluids (e.g., usingradio-frequency transmitters and sensors), among others.

Other example wearable medical devices include automated cardiacmonitors and/or defibrillators for use in certain specialized conditionsand/or environments such as in combat zones or within emergencyvehicles. Such devices can be configured so that they can be usedimmediately (or substantially immediately) in a life-saving emergency.In some examples, the ambulatory medical devices described herein can bepacing-enabled, e.g., capable of providing therapeutic pacing pulses tothe patient. In some examples, the ambulatory medical devices can beconfigured to monitor for and/or measure ECG metrics including, forexample, heart rate (such as average, median, mode, or other statisticalmeasure of the heart rate, and/or maximum, minimum, resting,pre-exercise, and post-exercise heart rate values and/or ranges), heartrate variability metrics, PVC burden or counts, atrial fibrillationburden metrics, pauses, heart rate turbulence, QRS height, QRS width,changes in a size or shape of morphology of the ECG information, cosineR-T, artificial pacing, QT interval, QT variability, T wave width, Twave alternans, T-wave variability, and ST segment changes.

FIG. 10 illustrates an example component-level view of a medical devicecontroller 1000 included in, for example, a wearable medical device. Asfurther shown in FIG. 10 , the therapy delivery circuitry 1002 can becoupled to one or more electrodes 1020 configured to provide therapy tothe patient. For example, the therapy delivery circuitry 1002 caninclude, or be operably connected to, circuitry components that areconfigured to generate and provide an electrical therapeutic shock. Thecircuitry components can include, for example, resistors, capacitors,relays and/or switches, electrical bridges such as an h-bridge (e.g.,including a number of insulated gate bipolar transistors or IGBTs),voltage and/or current measuring components, and other similar circuitrycomponents arranged and connected such that the circuitry componentswork in concert with the therapy delivery circuitry and under control ofone or more processors (e.g., processor 1018) to provide, for example,at least one therapeutic shock to the patient including one or morepacing, cardioversion, or defibrillation therapeutic pulses.

Pacing pulses can be used to treat cardiac arrhythmia conditions such asbradycardia (e.g., less than 30 beats per minute) and tachycardia (e.g.,more than 150 beats per minute) using, for example, fixed rate pacing,demand pacing, anti-tachycardia pacing, and the like. Defibrillationshocks can be used to treat ventricular tachycardia and/or ventricularfibrillation.

For example, each defibrillation shock can deliver between 60 to 180joules of energy. In some implementations, the defibrillating shock canbe a biphasic truncated exponential waveform, whereby the signal canswitch between a positive and a negative portion (e.g., chargedirections). This type of waveform can be effective at defibrillatingpatients at lower energy levels when compared to other types ofdefibrillation shocks (e.g., such as monophasic shocks). For example, anamplitude and a width of the two phases of the energy waveform can beautomatically adjusted to deliver a precise energy amount (e.g., 150joules) regardless of the patient’s body impedance. The therapy deliverycircuitry 1002 can be configured to perform the switching and pulsedelivery operations, e.g., under control of the processor 1018. As theenergy is delivered to the patient, the amount of energy being deliveredcan be tracked. For example, the amount of energy can be kept to apredetermined constant value even as the pulse waveform is dynamicallycontrolled based on factors such as the patient’s body impedance whichthe pulse is being delivered.

In certain examples, the therapy delivery circuitry 1002 can beconfigured to deliver a set of cardioversion pulses to correct, forexample, an improperly beating heart. When compared to defibrillation asdescribed above, cardioversion typically includes a less powerful shockthat is delivered at a certain frequency to mimic a heart’s normalrhythm.

A data storage region 1004 can include one or more of non-transitorycomputer-readable media, such as flash memory, solid state memory,magnetic memory, optical memory, cache memory, combinations thereof, andothers. The data storage 1004 can be configured to store executableinstructions and data used for operation of the medical devicecontroller 1000. In certain examples, the data storage 1004 can includeexecutable instructions that, when executed, are configured to cause theprocessor 1018 to perform one or more operations. In some examples, thedata storage 1004 can be configured to store information such as ECGdata as received from, for example, a sensing electrode interface 1012.

In some embodiments, a network interface 1006 can facilitate thecommunication of information between the medical device controller 1000and one or more other devices or entities over a communications network.For example, where the medical device controller 1000 is included in anambulatory medical device, the network interface 1006 can be configuredto communicate with a remote computing device such as a remote server orother similar computing device. In further embodiments, the remotecomputing device can be part of a remote data analytics system 1032. Thenetwork interface 1006 can include communications circuitry fortransmitting data in accordance with a Bluetooth® or Zigbee^(®) wirelessstandard for exchanging such data over short distances to anintermediary device 1034. In some examples, the intermediary device 1034can be configured as a base station, a “hotspot” device, a smartphone, atablet, a portable computing device, and/or other devices in proximityof the wearable medical device including the medical device controller1000. The intermediary device(s) 1034 may in turn communicate the datato a remote server over a broadband cellular network communicationslink, such as the data analytics system 1032. The communications linkmay implement broadband cellular technology (e.g., 2.5G, 2.75G, 3G, 4G,5G cellular standards) and/or Long-Term Evolution (LTE) technology orGSM/EDGE and UMTS/HSPA technologies for high-speed wirelesscommunication. In some implementations, the intermediary device(s) 1034may communicate with a remote server over a Wi-Fi^(®) communicationslink based on the IEEE 802.11 standard.

In certain embodiments, a user interface 1008 can include one or morephysical interface devices such as input devices, output devices, andcombination input/output devices and a software stack configured todrive operation of the devices. These user interface elements can rendervisual (e.g. LEDs 1042), audio (e.g., speaker 1040), and/or tactilecontent. Thus, the user interface 1008 can receive input (e.g., via oneor more control buttons 1044) or provide output, thereby enabling a userto interact with the medical device controller 1000.

The medical device controller 1000, in some embodiments, includes atleast one power source (e.g., rechargeable battery) 1010 configured toprovide power to one or more components integrated in the medical devicecontroller 1000. The battery 1010 can include a rechargeable multi-cellbattery pack. In one example implementation, the battery 1010 caninclude three or more 2200 mAh lithium ion cells that provide electricalpower to the other device components within the medical devicecontroller 1000. For example, the battery 1010 can provide its poweroutput in a range of between 20 mA to 1000 mA (e.g., 40 mA) output andcan support 24 hours, 48 hours, 72 hours, or more, of runtime betweencharges. In certain implementations, the battery capacity, runtime, andtype (e.g., lithium ion, nickel-cadmium, or nickel-metal hydride) can bechanged to best fit the specific application of the medical devicecontroller 1000.

A sensor interface 1012, in some embodiments, includes physiologicalsignal circuitry that is coupled to one or more sensors configured tomonitor one or more physiological parameters of the patient. As shown,the sensors 1022, 1024, 1026, 1030 can be coupled to the medical devicecontroller 1000 via a wired or wireless connection. The sensors caninclude one or more ECG sensing electrodes 1022 and non-ECGphysiological sensors such as a vibration sensor 1024, tissue fluidmonitor(s) 1026 (e.g., based on ultra-wide band RF devices), and motionsensor(s) 1030 (e.g., accelerometers, gyroscopes, and/or magnetometers).In some implementations, the sensors can include a number ofconventional ECG sensing electrodes 1022 in addition to digital sensingelectrodes 1022.

The sensing electrodes 1022 can be configured to monitor a patient’s ECGinformation. For example, by design, the digital sensing electrodes 1022can include skin-contacting electrode surfaces that may be deemedpolarizable or non-polarizable depending on a variety of factorsincluding the metals and/or coatings used in constructing the electrodesurface. All such electrodes can be used with the principles,techniques, devices and systems described herein. For example, theelectrode surfaces can be based on stainless steel, noble metals such asplatinum, or Ag-AgCl.

In some examples, the electrodes 1022 can be used with an electrolyticgel dispersed between the electrode surface and the patient’s skin. Incertain implementations, the electrodes 1022 can be dry electrodes thatdo not need an electrolytic material. As an example, such a dryelectrode can be based on tantalum metal and having a tantalum pentoxidecoating as is described above. Such dry electrodes can be morecomfortable for long term monitoring applications.

The vibration sensor(s) 1024, in some implementations, can be configuredto detect cardiac or pulmonary vibration information. For example, thevibration sensor(s) 1024 can detect a patient’s heart valve vibrationinformation. For example, the vibration sensor(s) 1024 can be configuredto detect cardio-vibrational signal values including any one or all ofS1, S2, S3, and S4. From these cardio-vibrational signal values or heartvibration values, certain heart vibration metrics may be calculated,including any one or more of electromechanical activation time (EMAT),average EMAT, percentage of EMAT (% EMAT), systolic dysfunction index(SDI), and left ventricular systolic time (LVST). The vibrationsensor(s) 1024 can also be configured to detect heart wall motion, forinstance, by placement of the sensor in the region of the apical beat.The vibration sensor(s) 1024 can include a vibrational sensor configuredto detect vibrations from a subject’s cardiac and pulmonary system andprovide an output signal responsive to the detected vibrations of atargeted organ, for example, being able to detect vibrations generatedin the trachea or lungs due to the flow of air during breathing. Incertain implementations, additional physiological information can bedetermined from pulmonary-vibrational signals such as, for example, lungvibration characteristics based on pulmonary vibrations produced withinthe lungs (e.g., stridor, crackle, etc.). The vibration sensor(s) 1024can also include a multi-channel accelerometer, for example, athree-channel accelerometer configured to sense movement in each ofthree orthogonal axes such that patient movement/body position can bedetected and correlated to detected cardio-vibrations information. Thevibration sensor(s) 1024 can transmit information descriptive of thecardio-vibrations information to the sensor interface 1012 forsubsequent analysis.

The tissue fluid monitor(s) 1026 can use radio frequency (RF) basedtechniques to assess fluid levels and accumulation in a patient’s bodytissue. For example, the tissue fluid monitor(s) 1026 can be configuredto measure fluid content in the lungs, typically for diagnosis andfollow-up of pulmonary edema or lung congestion in heart failurepatients. The tissue fluid monitor(s) 1026 can include one or moreantennas configured to direct RF waves through a patient’s tissue andmeasure output RF signals in response to the waves that have passedthrough the tissue. In certain implementations, the output RF signalsinclude parameters indicative of a fluid level in the patient’s tissue.The tissue fluid monitor(s) 1026 can transmit information descriptive ofthe tissue fluid levels to the sensor interface 1012 for subsequentanalysis.

In certain implementations, a cardiac event detector 1016 can beconfigured to monitor a patient’s ECG signal for an occurrence of acardiac event such as an arrhythmia or other similar cardiac event. Thecardiac event detector 1016 can be configured to operate in concert withthe processor 1018 to execute one or more methods that process receivedECG signals from, for example, the sensing electrodes 1022 and determinethe likelihood that a patient is experiencing a cardiac event. Thecardiac event detector 1016 can be implemented using hardware or acombination of hardware and software. For instance, in some examples,cardiac event detector 1016 can be implemented as a software componentthat is stored within the data storage 1004 and executed by theprocessor 1018. In this example, the instructions included in thecardiac event detector 1016 can cause the processor 1018 to perform oneor more methods for analyzing a received ECG signal to determine whetheran adverse cardiac event is occurring. In other examples, the cardiacevent detector 1016 can be an application-specific integrated circuit(ASIC) that is coupled to the processor 1018 and configured to monitorECG signals for adverse cardiac event occurrences. Thus, examples of thecardiac event detector 1016 are not limited to a particular hardware orsoftware implementation.

In some implementations, the processor 1018 includes one or moreprocessors (or one or more processor cores) that each are configured toperform a series of instructions that result in manipulated data and/orcontrol the operation of the other components of the medical devicecontroller 1000. In some implementations, when executing a specificprocess (e.g., cardiac monitoring), the processor 1018 can be configuredto make specific logic-based determinations based on input data receivedand be further configured to provide one or more outputs that can beused to control or otherwise inform subsequent processing to be carriedout by the processor 1018 and/or other processors or circuitry withwhich processor 318 is communicatively coupled. Thus, the processor 1018reacts to specific input stimulus in a specific way and generates acorresponding output based on that input stimulus. In some examplecases, the processor 1018 can proceed through a sequence of logicaltransitions in which various internal register states and/or other bitcell states internal or external to the processor 1018 can be set tologic high or logic low. As referred to herein, the processor 1018 canbe configured to execute a function where software is stored in a datastore coupled to the processor 1018, the software being configured tocause the processor 1018 to proceed through a sequence of various logicdecisions that result in the function being executed. The variouscomponents that are described herein as being executable by theprocessor 1018 can be implemented in various forms of specializedhardware, software, or a combination thereof. For example, the processor1018 can be a digital signal processor (DSP) such as a 24-bit DSP. Theprocessor 1018 can be a multi-core processor, e.g., having two or moreprocessing cores. The processor 1018 can be an Advanced RISC Machine(ARM) processor such as a 32-bit ARM processor or a 64-bit ARMprocessor. The processor 1018 can execute an embedded operating system,and include services provided by the operating system that can be usedfor file system manipulation, display & audio generation, basicnetworking, firewalling, data encryption and communications.

As noted above, an ambulatory medical device such as a WCD can bedesigned to include a digital front-end where analog signals sensed byskin-contacting electrode surfaces of a set of digital sensingelectrodes are converted to digital signals for processing. Typicalambulatory medical devices with analog front-end configurations usecircuitry to accommodate a signal from a high source impedance from thesensing electrode (e.g., having an internal impedance range fromapproximately 100 Kiloohms to one or more Megaohms). This high sourceimpedance signal is processed and transmitted to a monitoring devicesuch as processor 1018 of the controller 1000 as described above forfurther processing. In certain implementations, the monitoring device,or another similar processor such as a microprocessor or anotherdedicated processor operably coupled to the sensing electrodes, can beconfigured to receive a common noise signal from each of the sensingelectrodes, sum the common noise signals, invert the summed common noisesignals and feed the inverted signal back into the patient as a drivenground using, for example, a driven right leg circuit to cancel outcommon mode signals.

FIG. 11A illustrates an example medical device 1100 that is external,ambulatory, and wearable by a patient 1102, and configured to implementone or more configurations described herein. For example, the medicaldevice 1100 can be a non-invasive medical device configured to belocated substantially external to the patient. Such a medical device1100 can be, for example, an ambulatory medical device that is capableof and designed for moving with the patient as the patient goes abouthis or her daily routine. For example, the medical device 1100 asdescribed herein can be bodily-attached to the patient such as theLifeVest® wearable cardioverter defibrillator available from ZOLL®Medical Corporation. Such wearable defibrillators typically are wornnearly continuously or substantially continuously for two to threemonths at a time. During the period of time in which they are worn bythe patient, the wearable defibrillator can be configured tocontinuously or substantially continuously monitor the vital signs ofthe patient and, upon determination that treatment is required, can beconfigured to deliver one or more therapeutic electrical pulses to thepatient. For example, such therapeutic shocks can be pacing,defibrillation, or transcutaneous electrical nerve stimulation (TENS)pulses.

The medical device 1100 can include one or more of the following: agarment 1110, one or more ECG sensing electrodes 1112, one or morenon-ECG physiological sensors such as the sensors 1024, 1026, 1030described in relation to FIG. 10 , one or more therapy electrodes 1114 aand 1114 b (collectively referred to herein as therapy electrodes 1114),a medical device controller 1120 (e.g., controller 1000 as describedabove in the discussion of FIG. 10 ), a connection pod 1130, a patientinterface pod 1140, a belt 1150, or any combination of these. In someexamples, at least some of the components of the medical device 1100 canbe configured to be affixed to the garment 1110 (or in some examples,permanently integrated into the garment 1110), which can be worn aboutthe patient’s torso. In some implementations, at least a portion of thecomponents of the medical device 1100 can be configured to be inwireless communication with other components of the medical device 1100.For example, the patient interface pod 1140 may be arranged as a remotecontrol interface for use by the patient and in wireless communicationwith the medical device controller 1120.

The medical device controller 1120 can be operatively coupled to thesensing electrodes 1112, which can be affixed to the garment 1110, e.g.,assembled into the garment 1110 or removably attached to the garment,for example using hook and loop fasteners, snaps, and/or Velcro. In someimplementations, the sensing electrodes 1112 can be permanentlyintegrated into the garment 1110. The medical device controller 1120 canbe operatively coupled to the therapy electrodes 1114. For example, thetherapy electrodes 1114 can also be assembled into the garment 1110, or,in some implementations, the therapy electrodes 1114 can be permanentlyintegrated into the garment 1110. In an example, the medical devicecontroller 1120 includes a patient user interface 1160 to allow apatient interface with the externally-worn device. For example, thepatient can use the patient user interface 1160 to respond to activityrelated questions, prompts, and surveys as described herein.

Component configurations other than those shown in FIG. 11A arepossible. For example, the sensing electrodes 1112 can be configured tobe attached at various positions about the body of the patient 1102. Thesensing electrodes 1112 can be operatively coupled to the medical devicecontroller 1120 through the connection pod 1130. In someimplementations, the sensing electrodes 1112 can be adhesively attachedto the patient 1102. In some implementations, the sensing electrodes1112 and at least one of the therapy electrodes 1114 can be included ona single integrated patch and adhesively applied to the patient’s body.

The sensing electrodes 1112 can be configured to detect one or morecardiac signals. Examples of such signals include ECG signals and/orother sensed cardiac physiological signals from the patient. In certainexamples, as described herein, the non-ECG physiological sensors 1113such as accelerometers, vibrational sensors, RF-based sensors, and othermeasuring devices for recording additional non-ECG physiologicalparameters. For example, as described above, the non-ECG physiologicalsensors may be configured to detect other types of patient physiologicalparameters and acoustic signals, such as tissue fluid levels,cardio-vibrations, lung vibrations, respiration vibrations, and/orpatient movement, etc.

In some examples, the therapy electrodes 1114 can also be configured toinclude sensors configured to detect ECG signals as well as otherphysiological signals of the patient. The connection pod 1130 can, insome examples, include a signal processor configured to amplify, filter,and digitize these cardiac signals prior to transmitting the cardiacsignals to the medical device controller 1120. One or more of thetherapy electrodes 1114 can be configured to deliver one or moretherapeutic defibrillating shocks to the body of the patient 1102 whenthe medical device 1100 determines that such treatment is warrantedbased on the signals detected by the sensing electrodes 1112 andprocessed by the medical device controller 1120. Example therapyelectrodes 1114 can include metal electrodes such as stainless-steelelectrodes that include one or more conductive gel deployment devicesconfigured to deliver conductive gel to the metal electrode prior todelivery of a therapeutic shock.

In some examples, the medical device 1100 can further includes one ormore motion sensors such as accelerometers 1162. As shown in FIG. 11A,in some examples an accelerometer 1162 can be integrated into one ormore of a sensing electrode 1112, a therapy electrode 1114, the medicaldevice controller 1120, and various other components of the medicaldevice 1100.

In some implementations, medical devices as described herein can beconfigured to switch between a therapeutic medical device and amonitoring medical device that is configured to only monitor a patient(e.g., not provide or perform any therapeutic functions). For example,therapeutic components such as the therapy electrodes 1114 andassociated circuitry can be optionally decoupled from (or coupled to) orswitched out of (or switched in to) the medical device 1100A. Forexample, a medical device can have optional therapeutic elements (e.g.,defibrillation and/or pacing electrodes, components, and associatedcircuitry) that are configured to operate in a therapeutic mode. Theoptional therapeutic elements can be physically decoupled from themedical device to convert the therapeutic medical device into amonitoring medical device for a specific use (e.g., for operating in amonitoring-only mode) or a patient. Alternatively, the optionaltherapeutic elements can be deactivated (e.g., via a physical or asoftware switch), essentially rendering the therapeutic medical deviceas a monitoring medical device for a specific physiologic purpose or aparticular patient. As an example of a software switch, an authorizedperson can access a protected user interface of the medical device andselect a preconfigured option or perform some other user action via theuser interface to deactivate the therapeutic elements of the medicaldevice.

FIG. 11B illustrates a hospital wearable defibrillator 1100B that isexternal, ambulatory, and wearable by the patient 1102. Hospitalwearable defibrillator 1100B can be configured in some implementationsto provide pacing therapy, e.g., to treat bradycardia, tachycardia, andasystole conditions. The hospital wearable defibrillator 1100B caninclude one or more ECG sensing electrodes 1112, one or more therapyelectrodes 1114, a medical device controller 1120 and a connection pod1130. For example, each of these components can be structured andfunction as like number components of the medical device 1100A of FIG.11A. For example, the electrodes 1112 a-1112 c, 1114 a, 1114 b caninclude disposable adhesive electrodes. For example, the electrodes 1112a, 1114 a, 1114 b can include sensing and therapy components disposed onseparate sensing and therapy electrode adhesive patches.

In some implementations, both sensing and therapy components can beintegrated and disposed on a same electrode adhesive patch that is thenattached to the patient. For example, the front adhesively attachabletherapy electrode 1114 a attaches to the front of the patient’s torso todeliver pacing or defibrillating therapy. Similarly, the back adhesivelyattachable therapy electrode 1114 b attaches to the back of thepatient’s torso. In an example scenario, at least three ECG adhesivelyattachable sensing electrodes 1112 a-1112 c can be attached to at leastabove the patient’s chest near the right arm, above the patient’s chestnear the left arm, and towards the bottom of the patient’s chest in amanner prescribed by a trained professional.

A patient being monitored by a hospital wearable defibrillator and/orpacing device may be confined to a hospital bed or room for asignificant amount of time (e.g., 75% or more of the patient’s stay inthe hospital). As a result, a user interface 1160 can be configured tointeract with a user other than the patient, e.g., a nurse, fordevice-related functions such as initial device baselining, setting andadjusting patient parameters, and changing the device batteries.

In some examples, the hospital wearable defibrillator 1100B can furtherinclude one or more motion sensors such as accelerometers 1162. As shownin FIG. 11B, in some examples an accelerometer 1162 can be integratedinto one or more of a sensing electrode 1112 a (e.g., integrated intothe same patch as the sensing electrode), a therapy electrode 1114 a(e.g., integrated into the same patch as the therapy electrode), themedical device controller 1120, the connection pod 1130, and variousother components of the hospital wearable defibrillator 1100B.

In some implementations, an example of a therapeutic medical device thatincludes a digital front-end in accordance with the systems and methodsdescribed herein can include a short-term defibrillator and/or pacingdevice. For example, such a short-term device can be prescribed by aphysician for patients presenting with syncope. A wearable defibrillatorcan be configured to monitor patients presenting with syncope by, e.g.,analyzing the patient’s physiological and cardiac activity for aberrantpatterns that can indicate abnormal physiological function. For example,such aberrant patterns can occur prior to, during, or after the onset ofsyncope. In such an example implementation of the short-term wearabledefibrillator, the electrode assembly can be adhesively attached to thepatient’s skin and have a similar configuration as the hospital wearabledefibrillator described above in connection with FIG. 11B.

FIGS. 11C and 11D illustrate example wearable patient monitoring deviceswith no treatment or therapy functions. For example, such devices areconfigured to monitor one or more physiological parameters of a patient,e.g., for remotely monitoring and/or diagnosing a condition of thepatient. For example, such physiological parameters can include apatient’s ECG information, tissue (e.g., lung) fluid levels,cardio-vibrations (e.g., using accelerometers or microphones), and otherrelated cardiac information. A cardiac monitoring device is a portabledevice that the patient can carry around as he or she goes about theirdaily routine.

Referring to FIG. 11C, an example wearable patient monitoring device1100C can include tissue fluid monitors 1165 that use RF basedtechniques to assess fluid levels and accumulation in a patient’s bodytissue. Such tissue fluid monitors 1165 can be configured to measurefluid content in the lungs, typically for diagnosis and follow-up ofpulmonary edema or lung congestion in heart failure patients. The tissuefluid monitors 1165 can include one or more antennas configured todirect RF waves through a patient’s tissue and measure output RF signalsin response to the waves that have passed through the tissue. In certainimplementations, the output RF signals include parameters indicative ofa fluid level in the patient’s tissue. In examples, device 1100C may bea cardiac monitoring device that also includes digital sensingelectrodes 1170 a, 1170 b for sensing ECG activity of the patient.Device 1100C can pre-process the ECG signals via one or more ECGprocessing and/or conditioning circuits such as an ADC, operationalamplifiers, digital filters, signal amplifiers under control of amicroprocessor. Device 1100C can transmit information descriptive of theECG activity and/or tissue fluid levels via a network interface to aremote server for analysis. Additionally, in certain implementations,the device 1100C can include one or accelerometers 1162 for measuringmotion signals as described herein.

Referring to FIG. 11D, another example wearable cardiac monitoringdevice 1100D can be attached to a patient 1102 via at least threeadhesive digital cardiac sensing electrodes 1175 a-c disposed about thepatient’s torso. Additionally, in certain implementations, the device1100D can include one or accelerometers (not illustrated) integratedinto, for example, one or more of the digital sensing electrodes formeasuring motion signals as described herein.

Cardiac devices 1100C and 1100D are used in cardiac monitoring andtelemetry and/or continuous cardiac event monitoring applications, e.g.,in patient populations reporting irregular cardiac symptoms and/orconditions. These devices can transmit information descriptive of theECG activity and/or tissue fluid levels via a network interface to aremote server for analysis. Example cardiac conditions that can bemonitored include atrial fibrillation (AF), bradycardia, tachycardia,atrio-ventricular block, Lown-Ganong-Levine syndrome, atrial flutter,sino-atrial node dysfunction, cerebral ischemia, pause(s), and/or heartpalpitations. For example, such patients may be prescribed a cardiacmonitoring for an extended period of time, e.g., 10 to 30 days, or more.In some ambulatory cardiac monitoring and/or telemetry applications, aportable cardiac monitoring device can be configured to substantiallycontinuously monitor the patient for a cardiac anomaly, and when such ananomaly is detected, the monitor can automatically send data relating tothe anomaly to a remote server. The remote server may be located withina 24-hour manned monitoring center, where the data is interpreted byqualified, cardiac-trained reviewers and/or HCPs, and feedback providedto the patient and/or a designated HCP via detailed periodic orevent-triggered reports. In certain cardiac event monitoringapplications, the cardiac monitoring device is configured to allow thepatient to manually press a button on the cardiac monitoring device toreport a symptom. For example, a patient can report symptoms such as askipped beat, shortness of breath, light headedness, racing heart rate,fatigue, fainting, chest discomfort, weakness, dizziness, and/orgiddiness. The cardiac monitoring device can record predeterminedphysiologic parameters of the patient (e.g., ECG information) for apredetermined amount of time (e.g., 1-30 minutes before and 1-30 minutesafter a reported symptom). As noted above, the cardiac monitoring devicecan be configured to monitor physiologic parameters of the patient otherthan cardiac related parameters. For example, the cardiac monitoringdevice can be configured to monitor, for example, cardio-vibrationalsignals (e.g., using accelerometers or microphones),pulmonary-vibrational signals, breath vibrations, sleep relatedparameters (e.g., snoring, sleep apnea), tissue fluids, among others.

In some examples, the devices described herein (e.g., FIGS. 11A-11D) cancommunicate with a remote server via an intermediary or gateway device1180 such as that shown in FIG. 11D. For instance, devices such as shownin FIGS. 11A-D can be configured to include a network interfacecommunications capability as described herein in reference to, forexample, FIG. 10 .

Additionally, the devices 1100A-D described herein in relation to FIGS.11A-11D can be configured to include one or more vibrational sensors asdescribed herein for collecting signals for use in producingcardio-vibrational image matrices.

Reference has been made to illustrations representing methods andsystems according to implementations of this disclosure. Aspects thereofmay be implemented by computer program instructions. These computerprogram instructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/operations specified in the illustrations.

One or more processors can be utilized to implement various functionsand/or algorithms described herein. Additionally, any functions and/oralgorithms described herein can be performed upon one or more virtualprocessors, for example on one or more physical computing systems suchas a computer farm or a cloud drive.

Aspects of the present disclosure may be implemented by hardware logic(where hardware logic naturally also includes any necessary signalwiring, memory elements and such), with such hardware logic able tooperate without active software involvement beyond initial systemconfiguration and any subsequent system reconfigurations (e.g., fordifferent object schema dimensions). The hardware logic may besynthesized on a reprogrammable computing chip such as a fieldprogrammable gate array (FPGA) or other reconfigurable logic device. Inaddition, the hardware logic may be hard coded onto a custom microchip,such as an application-specific integrated circuit (ASIC). In otherembodiments, software, stored as instructions to a non-transitorycomputer-readable medium such as a memory device, on-chip integratedmemory unit, or other non-transitory computer-readable storage, may beused to perform at least portions of the herein described functionality.

Various aspects of the embodiments disclosed herein are performed on oneor more computing devices, such as a laptop computer, tablet computer,mobile phone or other handheld computing device, or one or more servers.Such computing devices include processing circuitry embodied in one ormore processors or logic chips, such as a central processing unit (CPU),graphics processing unit (GPU), field programmable gate array (FPGA),application-specific integrated circuit (ASIC), or programmable logicdevice (PLD). Further, the processing circuitry may be implemented asmultiple processors cooperatively working in concert (e.g., in parallel)to perform the instructions of the inventive processes described above.

The process data and instructions used to perform various methods andalgorithms derived herein may be stored in non-transitory (i.e.,non-volatile) computer-readable medium or memory. The claimedadvancements are not limited by the form of the computer-readable mediaon which the instructions of the inventive processes are stored. Forexample, the instructions may be stored on CDs, DVDs, in FLASH memory,RAM, ROM, PROM, EPROM, EEPROM, hard disk or any other informationprocessing device with which the computing device communicates, such asa server or computer. The processing circuitry and stored instructionsmay enable the computing device to perform, in some examples, the method100 of FIG. 1 , the method 300 of FIGS. 3A and 3B, the process flow 500of FIG. 5 , the process flow 600 of FIG. 6 , the process flow 800 ofFIG. 8 , the process flow 900 of FIG. 9A, and/or the process flow 920 ofFIG. 9B.

These computer program instructions can direct a computing device orother programmable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablemedium produce an article of manufacture including instruction meanswhich implement the function/operation specified in the illustratedprocess flows.

Embodiments of the present description rely on network communications.As can be appreciated, the network can be a public network, such as theInternet, or a private network such as a local area network (LAN) orwide area network (WAN) network, or any combination thereof and can alsoinclude PSTN or ISDN sub-networks. The network can also be wired, suchas an Ethernet network, and/or can be wireless such as a cellularnetwork including EDGE, 3G, 4G, and 5G wireless cellular systems. Thewireless network can also include Wi-Fi^(®), Bluetooth^(®), Zigbee^(®),or another wireless form of communication. The network, for example, maysupport communications between the medical device 502 and one or more ofthe engines as described in relation to FIG. 5 or between the graphicuser interface generation engine and the computing device having thedisplay 612, as described in relation to FIG. 6 . The network maysupport communications between the medical device controller 1000 andthe data analytics system 1032 and/or the intermediate device 1034 asdescribed in relation to FIG. 10 .

The computing device further includes a display controller forinterfacing with a display, such as a built-in display or LCD monitor. Ageneral purpose I/O interface of the computing device may interface witha keyboard, a hand-manipulated movement tracked I/O device (e.g., mouse,virtual reality glove, trackball, joystick, etc.), and/or touch screenpanel or touch pad on or separate from the display. The displaycontroller and display may enable presentation of the screen shotsillustrated, in some examples, in FIGS. 2A, 2B, 4, 7A, and 7B.

Moreover, the present disclosure is not limited to the specific circuitelements described herein, nor is the present disclosure limited to thespecific sizing and classification of these elements. For example, theskilled artisan will appreciate that the circuitry described herein maybe adapted based on changes on battery sizing and chemistry or based onthe requirements of the intended back-up load to be powered.

The functions and features described herein may also be executed byvarious distributed components of a system. For example, one or moreprocessors may execute these system functions, where the processors aredistributed across multiple components communicating in a network. Thedistributed components may include one or more client and servermachines, which may share processing, in addition to various humaninterface and communication devices (e.g., display monitors, smartphones, tablets, personal digital assistants (PDAs)). The network may bea private network, such as a LAN or WAN, or may be a public network,such as the Internet. Input to the system may be received via directuser input and received remotely either in real-time or as a batchprocess.

Although provided for context, in other implementations, methods andlogic flows described herein may be performed on modules or hardware notidentical to those described. Accordingly, other implementations arewithin the scope that may be claimed.

In some implementations, a cloud computing environment, such as GoogleCloud Platform™, may be used perform at least portions of methods oralgorithms detailed above. The processes associated with the methodsdescribed herein can be executed on a computation processor, of a datacenter. The data center, for example, can also include an applicationprocessor that can be used as the interface with the systems describedherein to receive data and output corresponding information. The cloudcomputing environment may also include one or more databases or otherdata storage, such as cloud storage and a query database. In someimplementations, the cloud storage database, such as the Google CloudStorage, may store processed and unprocessed data supplied by systemsdescribed herein. For example, the contents of the data repository 512,image matrix repository 540, mappings of parameter values and pixelcharacteristic values 536, and/or metrics repository 556 of FIG. 5 , thearrhythmia classifiers 804 and/or arrhythmia classifications 814 of FIG.8 , the cardiac risk biomarker classifiers 904, patient demographicinformation 910, and/or patient physiological metrics 916 of FIG. 1 ,and/or the heart failure classifiers 924, historic heart failureclassifications 928, and/or historic image matrix repository 940 may bemaintained in a database structure.

The systems described herein may communicate with the cloud computingenvironment through a secure gateway. In some implementations, thesecure gateway includes a database querying interface, such as theGoogle BigQuery platform. The data querying interface, for example, maysupport access by the ECG signal graph generation engine(s) 602 and/orthe cardio-vibrational signal graph generation engine(s) 604 to the datarepository 512, access by the image matrix graph generation engine(s) tothe image matrix repository, and/or access by the metrics presentationengine(s) 608 to the metrics repository 556 as described in relation toFIG. 6 . The data querying interface, in another example, may supportaccess by the arrhythmia machine learning engine(s) 802 to the imagematrix repository 540 and/or access by the medical device actiondetermination engine 808 to the metrics repository 556 as described inrelation to FIG. 8 . In a further example, the data querying interfacemay support access by the heart risk machine learning engine(s) 902 tothe image matrix repository 540 and/or access by the heart risk analysisengine 908 to one or more of the metrics repository 556, the patientdemographic information 910, and/or the patient physiological metrics916, as described in relation to FIG. 9A. The data querying interface,in an additional example, may support access by the heart failureprogression machine learning engine(s) 922 to the historic image matrixrepository 940 and/or the heart failure trend analysis engine 930 to themetrics repository 556 and/or the historic heart failure classifications928, as described in relation to FIG. 9B.

While certain embodiments have been described, these embodiments havebeen presented by way of example only, and are not intended to limit thescope of the present disclosures. Indeed, the novel methods, apparatusesand systems described herein can be embodied in a variety of otherforms; furthermore, various omissions, substitutions and changes in theform of the methods, apparatuses and systems described herein can bemade without departing from the spirit of the present disclosures. Theaccompanying claims and their equivalents are intended to cover suchforms or modifications as would fall within the scope and spirit of thepresent disclosures.

What is claimed is:
 1. (canceled)
 2. A system for monitoring a cardiaccondition of a patient using cardio-vibrational image matrixrepresentations of cardio-vibrational signals, the system comprising: awearable medical device comprising at least one vibrational sensorconfigured for monitoring a heart of the patient; a non-volatilecomputer-readable storage medium configured to store a plurality ofcardio-vibrational measurements; at least one machine learning engine,each machine learning engine trained to identify at least one type of aset of types of cardiac conditions including at least an existence of atleast one type of arrhythmia condition and a nonexistence of cardiacarrhythmia condition through analyzing cardio-vibrational imagematrices; and a plurality of operations stored as a plurality ofcomputer-executable instructions to a non-transitory computer-readablemedia and/or encoded in hardware logic, wherein the plurality ofoperations is configured to, in real-time, obtain the plurality ofcardio-vibrational measurements derived from cardio-vibrational signalsof the patient collected by the at least one vibrational sensor, store,to the non-volatile computer-readable storage medium, the plurality ofcardio-vibrational measurements, create a cardio-vibrational imagematrix having a plurality of pixel characteristic values correspondingto the plurality of cardio-vibrational measurements, thecardio-vibrational image matrix graphically representing a timeprogression of a plurality of adjacent cardiac portions, a same at leastone deflection feature being captured in each cardiac portion, and usingone or more machine learning engines of the at least one machinelearning engine, classify the contents of the cardio-vibrational imagematrix as a determined type of the set of types of cardiac conditions;wherein, upon identifying that the determined type corresponds to one ofthe at least one type of arrhythmia condition, the plurality ofoperations is configured to initiate an electrical therapy via thewearable medical device.
 3. The system of claim 2, wherein the pluralityof operations is configured to divide the plurality ofcardio-vibrational measurements into each cardiac portion of theplurality of adjacent cardiac portions such that each cardiac portioncomprises the same at least one deflection feature.
 4. The system ofclaim 2, wherein the plurality of operations is configured to mapparameter values of the plurality of cardio-vibrational measurements tothe plurality of pixel characteristic values.
 5. The system of claim 2,wherein the plurality of pixel characteristic values comprises betweenaround at least 3 and 16 different colors.
 6. The system of claim 2,wherein the same at least one deflection feature is selected from an S1peak and an S2 peak.
 7. The system of claim 2, wherein the wearablemedical device comprises a wearable cardioverter defibrillator.
 8. Thesystem of claim 2, wherein the at least one type of arrhythmia conditioncomprises an arrhythmia classification including at least one of aduration, a rate, or a mechanism of arrhythmia.
 9. The system of claim2, wherein the at least one type of arrhythmia condition comprises atleast one of a supraventricular tachycardia (SVT), a ventriculartachycardia, ventricular fibrillation, tachycardia, bradycardia,asystole, a heart pause condition, pulseless electrical activity, oratrial fibrillation.
 10. The system of claim 2, wherein the wearablemedical device comprises the non-transitory computer readable mediaand/or hardware logic.
 11. The system of claim 2, wherein the electricaltherapy comprises at least one of a defibrillating shock or a pacingpulse.
 12. The system of claim 2, wherein the plurality of operations isconfigured to, upon identifying the determined type corresponds to oneof the at least one type of arrhythmia condition, issue a warning to aclinician, a caretaker, and/or a wearer of the wearable medical device.13. The system of claim 2, wherein the plurality of operations isconfigured to, upon identifying the determined type corresponds to oneof the at least one type of arrhythmia condition, select a pacingroutine.
 14. The system of claim 2, wherein obtaining the plurality ofcardio-vibrational measurements comprises obtaining at least apredetermined duration of cardio-vibrational measurements.
 15. Thesystem of claim 14, wherein the plurality of operations is configured todivide the cardio-vibrational measurements into the plurality ofadjacent cardiac portions each having a duration smaller than thepredetermined duration.
 16. The system of claim 2, wherein creating thecardio-vibrational image matrix comprises: plotting the plurality ofadjacent cardiac portions along a first axis; and plotting the pluralityof pixel characteristic values of each cardiac portion of the pluralityof adjacent cardiac portions on a second axis.
 17. The system of claim2, wherein the plurality of operations is configured to train at least afirst machine learning engine of the one or more machine learningengines at least in part using historic cardio-vibrational imagematrices generated from signals produced from monitoring the patient,thereby recognizing a unique cardiac signature of the patient.
 18. Asystem for monitoring a cardiac condition of a patient usingcardio-vibrational image matrix representations of cardio-vibrationalsignals, the system comprising: a wearable medical device comprising atleast one vibrational sensor configured for monitoring a heart of thepatient; a non-volatile computer-readable storage medium configured tostore a plurality of cardio-vibrational measurements; at least onemachine learning engine configured to apply one or more of a pluralityof cardiac risk biomarker classifiers, each cardiac risk biomarkerclassifier trained, using a pre-existing plurality of cardio-vibrationalimage matrices corresponding to each of a progression of heart failureclassifications, to identify at least one heart failure biomarker of aset of heart failure biomarkers through analyzing cardio-vibrationalimage matrices, wherein the set of heart failure biomarkers correspondto the progression of heart failure classifications; and a plurality ofoperations stored as a plurality of computer executable instructions toa non-transitory computer readable media and/or encoded in hardwarelogic, wherein the plurality of operations is configured to, inreal-time, obtain the plurality of cardio-vibrational measurementsderived from cardio-vibrational signals of the patient collected by theat least one vibrational sensor, store, to the non-volatilecomputer-readable storage medium, the plurality of cardio-vibrationalmeasurements, divide the plurality of cardio-vibrational measurementsinto a plurality of adjacent cardiac portions, a same at least onedeflection feature being captured in each cardiac portion of theplurality of adjacent cardiac portions, wherein the at least onedeflection feature is selected from an S1 peak and an S2 peak, mapparameter values of the plurality of cardio-vibrational measurements toa plurality of pixel characteristic values, create a cardio-vibrationalimage matrix of the mapped parameter values, the cardio-vibrationalimage matrix graphically representing a time progression of theplurality of adjacent cardiac portions, using one or more machinelearning engines of the at least one machine learning engine, screen thecontents of the cardio-vibrational image matrix for at least oneidentified biomarker of the set of heart failure biomarkers, anddetermine, based at least in part on the at least one identifiedbiomarker, a present classification of the progression of heart failureclassifications.
 19. The system of claim 18, wherein the plurality ofoperations further comprises comparing the present classification to atleast one historic classification of the patient to determine patientprogression related to the set of heart failure biomarkers.
 20. Thesystem of claim 18, wherein the set of heart failure biomarkers relatesto at least one of sudden cardiac arrest (SCA) or low ejection fraction(EF).
 21. The system of claim 18, wherein the set of heart failurebiomarkers comprises one or more of electromechanical activation overtime (EMAT), left ventricular systolic time (LVST), S3 intensity, or S3width.